Patent Application
20250068501
In some instances, failure modes associated with a system and/or system components may be analyzed to identify, detect, and/or determine diagnostic responses to the failure modes, or any manner in which the system and/or the system components may fail to operate properly. For example, failure modes effects and diagnostic analysis (FMEDA) techniques may consider the system and the system components, functionality of the system components, the failure modes of the system components, effects of the failure modes on the system, automatic diagnostic responses, and/or other analyses.
Such FMEDA may be used with respect to safety measures associated therewith to help improve the safety of a system. For example, the system and/or the system components may be used in a setting that includes regulations on performance such that the system and/or the system components may retain a threshold level of safety in operation. For instance, a hardware used in an automotive setting may have imposed failure rates and/or other performance metrics that may be associated with providing safety to a user of the hardware. In these instances, the FMEDA may be used to analyze, detect, and/or determine diagnostic responses to the failure modes that may be associated with the system to meet the imposed failure rates and/or other performance metrics.
Some current approaches of the FMEDA include examining the system, the system components (e.g., hardware, software), environments the system is used in, and manners in which the system, the system components, and the environments impact each other. For example, all elements included in design of a machine may be examined, such as every system, subsystem, system components, and parts. For instance, for a particular hardware part of a system, possible failure modes (e.g., any possible manners of failure) associated with the particular hardware part, effects of the failure modes on overall functionality of the system, ability of diagnostic capabilities of the system to detect the failure modes, ability of the system to mitigate the failure modes, and other aspects may be examined.
However, such an approach may be not as effective as complexity of the system components increase and/or a number of the system components included in the system increases. For example, as the number of the system components and/or the complexity increases, a number of the failure modes may increase, which may lead to increases in load times (e.g., load times of a system performing the analysis due to the increased number of failure modes per system component) and amounts of data (e.g., data storage footprints may increase), and/or a decrease in visibility of various levels of data (e.g., system level data, subsystem level data, and/or other data levels) associated with the system components. For example, a user seeking an analysis of a first subset of the system may have difficulty locating the failure modes associated with the first subset (and/or system level failures associated with the failure modes of the first subset) as there may be hundreds, thousands, and/or tens of thousands of the failure modes that may be applicable and others that may be inapplicable to the analysis the user is performing.
Further, in instances in which the user is attempting to analyze failure modes associated with a subset of the system (e.g., a subset of failure modes) and the system is configured to load additional failure modes relative to the subset of the failure modes, the loading of the additional failure modes may add additional and/or unnecessary processing time to the analysis procedure. For instance, the user may be seeking to analyze the first subset of the failure modes which may include less than a hundred failure modes. However, the system may load all other failure modes, which may include thousands or tens of thousands of failure modes, which may consume additional processing power and/or significantly increase an amount of time to load and/or analyze the first subset of the failure modes.
According to one or more embodiments of the present disclosure, one or more systems and/or methods may be configured to collapse depictions of one or more conditions (e.g., failure modes) associated with a system and/or one or more system components included in the system. For example, operations may include obtaining a set of conditions associated with the system, where the conditions may correspond to one or more system components of the system. The operations may also include obtaining a mapping between one or more individual conditions of the set of the conditions and one or more system classes. The system classes may correspond to one or more system level effects that may be caused by the individual conditions of the set of conditions.
In some embodiments, collapsing instructions may be obtained, the collapsing instructions identifying one or more of the system components and/or one or more of the system classes. For example, the collapsing instructions may specify the system components and/or the system classes to be collapsed together. In some embodiments, the collapsing instructions may be based at least on relationships between the system components. In some embodiments, the collapsing instructions may be generated by the system based on architectural and/or logical design of the system. In some embodiments, the collapsing instructions may be provided by a user. In these and other embodiments, the user may be provided with a recommended collapsing instructions by the system.
In these and other embodiments, a subset of conditions may be identified from the set of conditions based on the collapsing instructions. For example, the subset of conditions may include the individual conditions corresponding to one or more of the system classes and/or one or more of the system classes specified by the collapsing instructions. Additionally or alternatively, the operations may include generating a visualization within a graphical user interface (GUI) in which the visualization includes at least the subset of conditions. For example, the set of conditions may be collapsed (e.g., the individual conditions not viewable to the user) while the subset of conditions is un-collapsed (e.g., provided to the user for viewing and/or analyzing). In some embodiments, a user may interact with the visualization via the GUI. For example, the user may collapse and/or un-collapse different individual conditions for viewing and/or analyzing.
One or more embodiments of the present disclosure may help in improving usability, scalability, and/or configurability of FMEDA operations relative to a system that may be complex (e.g., the system including many system components and/or may failure modes) which may cause an improvement to one or more safety systems and/or operations associated with the system.
The present systems and methods for collapsing failure modes are described in detail below with reference to the attached figured, wherein:
Systems and methods related to collapsing a set of conditions during failure modes effects and diagnostic analysis (FMEDA) are disclosed in the present disclosure. In some embodiments, the conditions may correspond to one or more system components included in a system. In these and other embodiments, the one or more system components may include any hardware and/or software components of the system. In these and other embodiments, individual conditions corresponding to individual system components may include any potential problems or issues that the individual system components may have that may affect the system and/or one or more parts or components of the system. For example, a brake system may be one of the system components of an automotive system. In these instances, the conditions corresponding to the brake system may include loss of brake pressure, overheating brake pads, damaged rotor disks, leaking hydraulic fluid, environment conditions (e.g., driving through mud or water), overloading of the automotive system, among others. Additionally or alternatively, the conditions may include any errors that may stem from software issues such as a problem with automatic braking. In some embodiments, the conditions may be examined to analyze, detect, and/or determine diagnostic responses to the conditions. For example, FMEDA techniques may be performed to analyze the system, the system components, and the corresponding conditions.
In the present disclosure, a reference to conditions may also include a reference to failure modes. In some embodiments, the failure modes may include any manner in which the system and/or the system components may fail to operate properly. For example, the failure modes may define any types of failures (e.g., hardware, software, etc.) that the system and/or the system components may have. In these and other embodiments, individual system components may have multiple failure modes associated therewith.
The embodiments of the present disclosure may improve usability, scalability, and/or configurability of FMEDA. For example, in one or more embodiments of the present disclosure, FMEDA may be performed without loading an entire set of conditions for a particular system which may improve usability, scalability, and/or configurability. For instance, only a subset of conditions of the set of conditions may be un-collapsed (e.g., loaded and/or visualized) for analysis while rest of the set of conditions may be collapsed (e.g., not loaded and/or visualized) which may reduce processing power and/or processing time. Additionally or alternatively, by only un-collapsing the set of conditions, it may be easier to locate a particular condition or conditions that may be of interest. In some embodiments, the set of conditions may be grouped into one or more groups and/or subgroups based at least on relationships between the one or more system components corresponding to the individual conditions, which may make it easier to locate and un-collapse only the subset of conditions.
One or more embodiments of the present disclosure may help improve usability, scalability, and/or configurability over some traditional approaches to FMEDA. For example, some traditional approaches to FMEDA include obtaining and providing all possible conditions and/or failure modes related to a particular system. One or more conditions and/or failure modes may be selected for further analysis and/or examination. However, such an approach may become more burdensome for a system and/or a user as a number of the system components in the system and/or a number of the conditions for the system components increase. For instance, the number of the conditions may increase exponentially as the system gets more complex. In some embodiments, increased number of the conditions may require longer load times due to the increased number of the conditions. Additionally or alternatively, the increased number of the conditions may lead to increased amounts of data. For instance, a larger data storage footprint may be required to store the conditions.
Additionally or alternatively, under some traditional approaches, visibility during an analysis of the conditions may also be affected. For instance, a user may seek to perform an analysis of a particular condition by displaying the particular condition. For example, by displaying the particular condition for the analysis, the user may visualize the analysis for easier understanding of the particular condition. In these and other embodiments, the increased number of conditions may make it more difficult to locate a particular condition to be analyzed. For example, visibility into various levels of data (e.g., system level data, subsystem level data, and/or other data levels) associated with the system components may be buried in the large number of the conditions available. For instance, the user seeking an analysis of a first subset of the system may have difficulty locating the conditions associated with the first subset (and/or the system level failures associated with the conditions of the first subset) as there may be hundreds, thousands, and/or tens of thousands of the conditions that may be inapplicable to the analysis the user is performing.
One or more of the embodiments disclosed herein may be related to collapsing the conditions related to one or more ego-machines and/or components of the one or more ego-machines, which may include any applicable machine or system that is capable of performing one or more autonomous or semi-autonomous operations. Example ego-machines may include, but are not limited to, vehicles (land, sea, space, and/or air), robots, robotic platforms, etc. By way of example, the ego-machine computing applications may include one or more applications that may be executed by an autonomous vehicle or semi-autonomous vehicle, such as an example autonomous vehicle 400 (alternatively referred to herein as “vehicle 400” or “ego-machine 400”) described with respect to
The systems and methods described herein may be used by, without limitation, non-autonomous vehicles or machines, semi-autonomous vehicles or machines (e.g., in one or more adaptive driver assistance systems (ADAS)), autonomous vehicles or machines, piloted and un-piloted robots or robotic platforms, warehouse vehicles, off-road vehicles, vehicles coupled to one or more trailers, flying vessels, boats, shuttles, emergency response vehicles, motorcycles, electric or motorized bicycles, aircraft, construction vehicles, underwater craft, drones, and/or other vehicle types. Further, the systems and methods described herein may be used for a variety of purposes, by way of example and without limitation, for machine control, machine locomotion, machine driving, synthetic data generation, model training, perception, augmented reality, virtual reality, mixed reality, robotics, security and surveillance, simulation and digital twinning, autonomous or semi-autonomous machine applications, deep learning, environment simulation, object or actor simulation and/or digital twinning, generative AI, data center processing, conversational AI (such as by employing one or more language models such as one or more large language models (LLMs)), light transport simulation (e.g., ray-tracing, path tracing, etc.), collaborative content creation for 3D assets, cloud computing and/or any other suitable applications.
Disclosed embodiments may be comprised in a variety of different systems such as automotive systems (e.g., a control system for an autonomous or semi-autonomous machine, a perception system for an autonomous or semi-autonomous machine), systems implemented using a robot, aerial systems, medial systems, boating systems, smart area monitoring systems, systems for performing deep learning operations, systems for performing simulation operations, systems for performing digital twin operations, systems implemented using an edge device, systems incorporating one or more virtual machines (VMs), systems for performing synthetic data generation operations, systems implemented at least partially in a data center, systems for performing conversational AI operations (e.g., systems that implement one or more language models, such as large language models (LLMs)), systems for performing one or more generative AI operations, systems for hosting real-time streaming applications, systems for presenting one or more of virtual reality content, augmented reality content, or mixed reality content, systems for performing light transport simulation, systems for performing collaborative content creation for 3D assets, systems implemented at least partially using cloud computing resources, and/or other types of systems.
The embodiments of the present disclosure will be explained with reference to the accompanying figures. It is to be understood that the figures are diagrammatic and schematic representations of such example embodiments, and are not limiting, nor are they necessarily drawn to scale. In the figures, features with like numbers indicate like structure and function unless described otherwise.
With respect to
As detailed herein, in general, the FMEDA system 100 may include a consolidation module 110. In some embodiments, the consolidation module 110 may be configured to obtain a detailed FMEDA 135 and one or more consolidating parameters 115. In some embodiments, the consolidation module 110 may be further configured to generate a consolidated FMEDA 140 based at least on the detailed FMEDA 135 and the one or more collapsing parameters 115.
In some embodiments, one or more of the modules described herein may include code and routines configured to allow a computing system to perform one or more operations. Additionally or alternatively, one or more of the modules may be implemented using hardware including one or more processors, central processing units (CPUs) graphics processing units (GPUs), data processing units (DPUs), parallel processing units (PPUs), microprocessors (e.g., to perform or control performance of one or more operations), field-programmable gate arrays (FPGA), application-specific integrated circuits (ASICs), accelerators (e.g., deep learning accelerators (DLAs)), programmable vision accelerators (including one or more direct memory address (DMA) systems and/or vector processing units (VPUs)), and/or other processor types. In these and other embodiments, one or more of the modules may be implemented using a combination of hardware and software. In the present disclosure, operations described as being performed by a respective module may include operations that the respective module may direct a corresponding computing system to perform. In these or other embodiments, one or more of the modules may be implemented by one or more computing devices, such as that described in further detail with respect to
In some embodiments, the consolidation module 110 may be configured to obtain the detailed FMEDA 135. For instance, in some embodiments, the consolidation module 110 may obtain the detailed FMEDA 135 from a separate entity (e.g., program, system, software, hardware). For example, the consolidation module 110 may obtain the detailed FMEDA 135 from VC Functional Safety Manager (VCFSM) which may be configured to generate a list of conditions associated with a particular machine and/or a system implementing the FMEDA system 100. In some embodiments, the FMEDA system 100 may be configured to generate the detailed FMEDA 135 which may be obtained by the consolidation module 110.
In some embodiments, the detailed FMEDA 135 may be a report of one or more conditions related to the particular machine and/or the system implementing the FMEDA system 100. For example, the detailed FMEDA 135 may provide a list of different conditions and/or events that may cause the particular system to fail or operate improperly. In some embodiments, the one or more conditions may correspond to one or more system components included in the particular system. For example, the particular system may include one or more hardware and/or software components which may be associated with the one or more conditions. For example, individual system components may include different conditions and/or events that may cause the individual system components to fail. As an example, the particular system may include a computing system which may include system components such as a processor. In these instances, the detailed FMEDA 135 may include conditions related to the system components such as the processor. For instance, the conditions corresponding to the processor may include overheating, overclocking, power surges, improper installation, among others. Additionally or alternatively, the processor may fail due to bugs, ambiguities, misinterpretation and/or incompetent code, among others. In the present disclosure, a reference to the detailed FMEDA 135 “including” the one or more conditions may refer to the detailed FMEDA 135 representing a list including the one or more conditions.
In some embodiments, the consolidation module 110 may generate the consolidated FMEDA 140 based at least on the detailed FMEDA 135. For example, the consolidated FMEDA may include a different representation of the one or more conditions included in the detailed FMEDA 135. For instance, in some embodiments, the consolidated FMEDA 140 may collapse one or more conditions included in the detailed FMEDA 135. Additionally or alternatively, the consolidated FMEDA 140 may un-collapse only a subset of one or more conditions included in the FMEDA 140. In the present disclosure, collapsing may refer to not loading and/or hiding individual conditions from view, and un-collapsing may refer to loading and/or visualizing the individual conditions. For example, by collapsing the one or more conditions and un-collapsing only the subset of the one or more conditions, the consolidated FMEDA 140 may only load and/or visualize the one or more conditions of interest without loading an entire set of the one or more conditions. In these and other embodiments, by only loading the subset of the one or more conditions, required processing power and time may be reduced compared to loading the entire set. Additionally or alternatively, visibility of the individual conditions being analyzed may be improved by un-collapsing the subset of the one or more conditions. For instance, by loading the entire set of the one or more conditions, it may be difficult to locate and analyze individual conditions of interest. Contrastingly, by only loading the subset of the conditions, it may be easier and/or faster to locate the individual conditions of interest for further analysis.
In some embodiments, the consolidation module 110 may collapse the one or more conditions based at least on one or more groups. For example, the one or more conditions may be placed into the one or more groups where collapsing and/or un-collapsing a group collapse and/or un-collapses the one or more conditions placed in the group. In some embodiments, the one or more groups may include multiple levels. For example, the one or more groups may include subgroups and the subgroups may further include lower groups.
In some embodiments, the consolidation module 110 may obtain the one or more consolidating parameters 115 which may specify how to generate the consolidated FMEDA 140 based on the detailed FMEDA 135. For example, the consolidating parameters 115 may specify different standards and/or parameters to be followed in collapsing and/or un-collapsing the one or more conditions included in the detailed FMEDA 135. For instance, the consolidating parameters 115 may specify standards to follow in forming the one or more groups and how the one or more groups may be visualized.
In some embodiments, the consolidating parameters 115 may include a collapse scope 120 which may include example structures that may be used to collapse the one or more conditions. For example, the collapse scope 120 may include one or more grouping standards to be followed in forming the one or more groups and different structures of organizing the one or more groups. In some embodiments, the one or more grouping standards may be based at least on the one or more system components included in the particular system implementing the FMEDA system 100.
For example, the one or more grouping standards may be based at least on relationships between the one or more system components. For instance, the one or more system components may be grouped together based on how the one or more system components are related based on architectural and/or logical design of the particular system. In some instances, the one or more system components may be architecturally and/or logically related where the one or more system components are physically connected to each other and/or are logically connected such as transmitting and/or receiving data. In these and other embodiments, the one or more system components that are connected and/or are related may be grouped together. In these and other embodiments, by selecting a particular system component, the one or more conditions corresponding to the particular system component may be un-collapsed. For instance, the particular system component may be located among the one or more groups, and selecting the particular system component may provide the one or more conditions corresponding to the particular system component.
Additionally or alternatively, the collapse scope 120 may define different levels corresponding to the one or more system components or the different groups. For example, the one or more system components that are grouped together may be placed in the different levels. For instance, the particular system may be placed at the highest level which may be followed by main system components in a second level. The main components may include subcomponents which may be placed in a third level. In some embodiments, there may be any number of levels suitable for complexity and/or a number of system components that the particular machine may have.
For example, an example system may include a system-on-chip (SOC) which may be placed on the highest level. The SOC may include the main system components such as a central processing unit, memory, a graphical processing unit, input and output ports, peripheral interfaces, secondary storage devices, accelerators, among others, which may be placed in the second level. Additionally or alternatively, the main system components included in the SOC may include the subcomponents in the third level. For example, the central processing unit may include a control unit, arithmetic logic units, registers, buses, clocks, etc.
In some embodiments, the one or more grouping standards may be based at least on different effects that the one or more conditions may have on system level. For example, the one or more conditions may be grouped together based on how the individual conditions manifest at the system level. In these and other embodiments, the collapse scope 120 may include a low-level failure mode (FM) to system level effect class (SLEC) mapping (referred hereafter as “SLEC mapping”).
For example, the SLEC mapping may provide different effects the one or more conditions may have on the particular system. In these and other embodiments, the SLEC mapping may include one or more system level effect classes (hereafter referred to as “system classes”). For example, the one or more system classes may indicate different effects a particular condition may manifest on the system level. In these and other embodiments, the one or more conditions may be mapped (e.g., associated with) to a particular system class where the one or more conditions associated with the particular same system class may have same or similar effect on the particular system.
In these and other embodiments, the one or more conditions may be collapsed and/or un-collapsed based on the one or more system classes. For example, the one or more system classes may include the one or more conditions associated with the one or more system classes. By collapsing and/or un-collapsing the one or more system classes, the one or more conditions corresponding to the one or more system classes may be collapsed and/or un-collapsed. Additionally or alternatively, the one or more conditions may include a reference to the corresponding system components. For example, by selecting on a specific condition, a reference to a specific system component corresponding to the specific condition may be provided.
In some embodiments, the collapse scope 120 may include a structure of displaying the one or more conditions. For example, the one or more conditions may be represented in a treelike structure, a graph structure, a hash structure, or a linear structure, among others. For example, in instances where the one or more conditions are grouped based on the relationships between the one or more system components, the one or more conditions may be represented and/or displayed based on the different levels corresponding to the one or more system components. For instance, using the treelike structure, a machine or a system may be placed at top of a tree and system components included in the machine or the system may branch out accordingly. For example, using the example of the SOC, the SOC may be placed at the top of the tree and system components such as the central processing unit, memory, a graphical processing unit, and input and output ports may branch out from the SOC. In these and other embodiments, the system components may additionally branch out into one or more subbranches where the one or more subbranches include one or more subcomponents included in the different system components. In these and other embodiments, by selecting a specific system component and/or subcomponent, the one or more conditions associated with the specific system component and/or the subcomponent may be loaded and/or displayed. An example treelike hierarchy of the one or more system components may be described in more detail with respect to
In some embodiments, the consolidating parameters 115 may include a collapse criteria 130. In these and other embodiments, the collapse criteria 130 may include one or more specific collapsing instructions. In some embodiments, one or more of the collapsing instructions may be generated based at least on the collapse scope 120. For example, the collapse criteria 130 may be generated using the example structures provided by the collapse scope 120. In some embodiments, the collapse criteria 130 may be same as the example structures provided in the collapse scope 120. For instance, the collapse criteria 130 may group the one or more conditions according to same standards as provided in the collapse scope 120. In some embodiments, the collapse criteria 130 may be slightly or largely different from the collapse scope 120. In some embodiments, the collapse criteria 130 may be completely different from the collapse scope 120.
In some embodiments, the collapse criteria 130 may be determined based at least on the collapse scope 120 and/or user inputs. For example, the consolidation module 110 may obtain the collapse scope 120, including the example structures, and the user inputs to modify the example structures. For example, the user inputs may relate to modifying the example structures to fit an analysis being performed. In these and other embodiments, the consolidation module 110 may modify the example structures based at least on the user inputs. In some instances, the user inputs may include instructions making minor changes to the example structures. In another instance, the user inputs may include instructions to modify a substantial part of the example structures. In yet another instance, the user inputs may include instructions for the consolidation module 110 to generate a new structure without using the example structures. In yet another instance, the user inputs may not indicate any modifications and/or may approve the example structures. In these and other embodiments, the user inputs may vary according to specific parameters and/or requirements associated with a specific analysis being performed. In some embodiments, the consolidation module 110 may be configured to generate the consolidated FMEDA 140 based at least on the collapse criteria 130. For example, the consolidation module 110 may apply the example structures (e.g., as modified by the user inputs) to the detailed FMEDA 135 to generate the consolidated FMEDA 140.
In some embodiments, the FMEDA system 100 may include a graphical user interface (GUI) 145. For example, the consolidation module 110 may be configured to generate the GUI 145 based at least on the consolidated FMEDA 140. For example, the GUI 145 may include a collapsed list of the one or more conditions corresponding to the consolidated FMEDA 140. In these and other embodiments, the GUI 145 may be presented on a display. For example, the collapsed list of the one or more conditions may be presented on the display. Additionally or alternatively, the user may interact with the GUI 145 presented on the display. For example, the user may interact with the consolidated FMEDA 140 through the GUI 145. For instance, the user may un-collapse the conditions that the user is interested in and perform an analysis on the conditions. In some embodiments, the FMEDA may be accessed via one or more application programming interfaces (APIs).
In some embodiments, the consolidation module 110 may obtain additional user inputs to modify the consolidated FMEDA 140. For example, the additional user inputs may relate to adding, removing, and/or editing the one or more conditions presented in the consolidated FMEDA 140. For instance, the additional user inputs may relate a missing condition and/or a redundant condition in the consolidated FMEDA. In these instances, the consolidation module 110 may add the missing condition and/or remove the redundant condition according to the additional user inputs. Additionally or alternatively, the additional user inputs may include instructions regarding safety mechanisms related to the conditions. For example, the additional user inputs may instruct the consolidation module 110 to add, remove, and/or edit safety mechanisms to the consolidated FMEDA based on the analysis performed. For instance, additional safety mechanisms may be beneficial for the conditions subject to the analysis. As an example, the analysis may be performed with respect to a processing unit, where the user may determine that a safety mechanism, such as different and/or additional cooling mechanisms may be beneficial and/or needed to further prevent overheating of the processing unit. In such instances, the consolidation module 110 may be instructed, by the additional user inputs, to add such safety mechanisms to the consolidated FMEDA 140.
In some embodiments, the consolidation module 110 may obtain the additional user inputs through the GUI 145. For instance, the user may provide the additional user inputs by interacting with the GUI 145. In some embodiments, the consolidation module 110 may be further configured to roll back any changes made to the consolidated FMEDA 140 based on the user inputs and/or the additional user inputs to the detailed FMEDA 135. For instance, any conditions and/or safety mechanisms added to the consolidated FMEDA 140 may be added to the detailed FMEDA 135. Additionally or alternatively, any conditions and/or safety mechanisms removed from the consolidated FMEDA 140 may also be removed from the detailed FMEDA 135.
In some embodiments, the FMEDA system 100 may include a checking module 150 configured to obtain the detailed FMEDA 135 and the consolidated FMEDA 140. In some embodiments, the checking module 150 may be further configured to verify that the one or more conditions and/or safety mechanisms present in the consolidated FMEDA 140 matches the one or more conditions and/or the safety mechanisms present in the detailed FMEDA 135. For example, the checking module 150 may verify that the consolidation module 110 properly integrated any changes made by the user on the consolidated FMEDA 140 to the detailed FMEDA 135. In response to verifying that the detailed FMEDA 135 and the consolidated FMEDA 140 match (e.g., included same list of conditions and/or safety mechanisms), the checking module 150 may generate consolidated FMEDA logs 155. In these and other embodiments, the consolidated FMEDA logs 155 may include updated set of conditions and/or safety mechanisms related to the particular system.
In some embodiments, the structure 200 may include a root 202. In these and other embodiments, the root 202 may indicate a starting point of the structure 20 and/or represent the system being represented by the structure 200. For example, the root 202 may include a type and/or a name of the system. For instance, the system may include a system-on-chip (SOC), and the structure 200 may represent a hierarchical representation of system components and/or corresponding conditions included in the SOC.
In some embodiments, the structure 200 may branch out (e.g., expand, extend, diverge, and/or separate into) from the root 202 to one or more branches. For example, the root 202 (e.g., the SOC) may branch out to the one or more branches representing the system components building up the SOC. As an example, the root 202 may branch out to at least a first branch 204a and a second branch 204b. In these and other embodiments, the first branch 204a and the second branch 204b may be collectively referred to as a “first-level branches 204”, indicating that the first branch 204a and the second branch 204b directly branch out from the root 202. In these and other embodiments, the first-level branches 204 may include one or more system components included in the system. For example, the SOC may include a central processing unit, a memory, a graphical processing unit, input and output ports, secondary storage interface, among others. As an example, the first branch 204a may include the processing unit, and the second branch 204b may include the memory.
In some embodiments, the first-level branches 204 may further branch out to one or more second-level branches. For example, the system components building up the SOC may branch out into subcomponents included in the system components. For instance, the first branch 204a may branch out to a third branch 208a and a fourth branch 208b, and the second branch 204b may branch out to a fifth branch 208c and a sixth branch 208d. In these and other embodiments, the third branch 208a, the fourth branch 208b, the fifth branch 208c, and the sixth branch 208d may be collectively referred to as the second-level branches 208. In these and other embodiments, the third branch 208a and the fourth branch 208b may include subcomponents of the processor. For instance, the subcomponents of the processor may include a control unit, arithmetic logic units, registers, buses, clocks, etc. In these instances, the third branch 208a may correspond to the control unit, and the fourth branch 208b may correspond to the arithmetic logic units.
In these and other embodiments, the fifth branch 208c and the sixth branch 208d may include subcomponents of the second branch 204b (e.g., the memory). For example, the fifth branch 208c may include a read-only memory (ROM), and the sixth branch 208d may include a random-access memory (RAM).
In some embodiments, the structure 200 may include any suitable number of the second-level branches 208. For instance, although the first branch 204a is illustrated as branching out two second-level branches (e.g., the third branch 208a and the fourth branch 208b), the first branch 204a may include any additional number of branches. For example, the first branch 204a may branch out to a number corresponding to a number of subcomponents (e.g., the graphical processing unit, the input and out ports, the secondary storage interface, etc.) included in the first branch 204a (e.g., the processor).
In some embodiments, the structure 200 may include any number of levels of branches suitable for the system. For example, the structure may further branch out to third-level branches, fourth-level branches, etc. In these and other embodiments, the levels of branches may differ among branches of same level. For instance, the first branch 204a may branch out to different levels of branches than the second branch 204b.
In some embodiments, the structure 200 may include one or more signifiers representative of relationships among the root 202 and/or the branches. For example, the signifiers may represent relationships between the SOC and the processor and/or the memory. Additionally or alternatively, the signifiers may represent relationships between the one or more system components. For example, the signifiers may represent the relationship between the processor and the memory. In some embodiments, the signifiers may represent the relationships as logic gates. For example, the signifiers may include an AND gate and/or a OR gate. For instance, a first signifier 206 may represent an OR gate. In these and other instances, the OR gate may indicate that the root 202 may fail (e.g., depart from proper operation) in response to the first branch 204a or the second branch 204b failing to operate properly. For instance, the processor may fail operating properly in response to the processor or the memory failing. In some embodiments, the first signifier 206 may be the AND gate, in which instance, the first branch 204a and the second branch 204b may need to both fail to cause the root 202 to fail. For instance, a failure of only one of the first branch 204a or the second branch 204b may not affect the root 202.
In some embodiments, the structure 200 may include one or more conditions corresponding to the branches. In these and other embodiments, the one or more conditions may represent how the system components and/or the subcomponent may fail. In some embodiments, the one or more conditions may be represented as stemming from the corresponding system components and/or subcomponents. For example, the third branch 208a may include a first condition 210. In these and other embodiments, the first condition 210 may include one of multiple conditions and/or issues that the control unit may have. For instance, the first condition may include at least one of a broken and/or bent pin, a short circuit, overheating, among others.
In some embodiments, the one or more branches (e.g., system components) and/or the corresponding conditions (e.g., the first-level branches 204 and/or the second-level branches 208) may be collapsible. For example, the one or more branches and/or the corresponding conditions may be hidden from view and/or not loaded as part of the structure 200. For instance, a GUI (e.g., the GUI 145 of
In some embodiments, one or more changes may be made to the structure 200. For example, system components, subcomponents, and/or conditions may be added, removed, and/or modified. For instance, it may be determined that a particular condition that should be associated with a particular subcomponent is missing and needs to be added. Additionally or alternatively, the particular condition may be no longer applicable and may need to be removed. In some embodiments, the changes may be made through interacting with the GUI representing the structure 200. For example, the GUI may be displayed on a display to be interacted with a user. In these instances, a consolidation module (e.g., such as the consolidation module 110 of
Modifications, additions, or omissions may be made to
The method 300 may include one or more blocks. Although illustrated with discrete blocks, the operations associated with one or more of the blocks of the method 300 may be divided into additional blocks, combined into fewer blocks, or eliminated, depending on the particular implementation.
In some embodiments, the method 300 may include block 302. At block 302, a first set of conditions associated with a particular system may be obtained. In some embodiments, the first set of conditions may correspond to one or more system components included in the particular system. For example, the first set of conditions may include different conditions and/or issues that the one or more system components and/or subcomponents included in the system components may have.
At block 304, a mapping between individual conditions of the first set of conditions and one or more system classes may be obtained. In some embodiments, the one or more system classes may correspond to one or more system level effects caused by the first set of conditions. For instance, the one or more system level effects may represent different effects the individual conditions may have on the particular system on a system level. For example, a particular condition associated with a particular system component may manifest a certain way at the system level. For instance, a particular system level effect may include a boot failure. In these instances, the individual conditions related to a processor and/or a memory that may cause the boot failure may be mapped to a system class corresponding to the boot failure.
In some embodiments, the first set of conditions may include one or more corresponding safety mechanisms. For example, the corresponding safety mechanisms may indicate how the set of conditions may be mitigated. For example, where the particular condition is associated with a boot failure, the corresponding safety mechanisms may include booting from a backup.
At block 306, collapsing instructions may be obtained. In some embodiments, the collapsing instructions may include a structure of collapsing. In some embodiments, the collapsing instructions may specify how the first set of conditions should be organized. For example, the collapsing instructions may define relationships between the one or more system components and/or between the one or more system classes. For instance, in some embodiments, the structure of collapsing may include a treelike hierarchy. Additionally or alternatively, the collapsing instructions may include one or more system components and/or one or more system classes of interest. For example, the one or more system components and/or the one or more system classes may be subject to a particular analysis being performed.
At block 308, a second set of conditions may be generated based at least on the first set of conditions and the collapsing instructions. In some embodiments, the second set of conditions may be configured to organize the individual conditions included in the first set of conditions according to the collapsing instructions and/or the structure of collapsing. For example, the second set of conditions may organize and/or represent the individual conditions in the treelike hierarchy. In some embodiments, the second set of conditions may be compared with the first set of conditions to verify and/or determine that the second set of conditions and the first set of conditions include same individual conditions. Additionally or alternatively, in response to the second set of conditions not matching the first set of conditions, differences may be mitigated by adding and/or removing the individual conditions from the first set of conditions and/or the second set of conditions.
At block 310, a subset of conditions may be generated based at least on the collapsing instructions from the second set of conditions. In some embodiments, the subset of conditions may include the individual conditions subject to the particular analysis. For instance, the collapsing instructions may specify the individual conditions subject to the particular analysis and/or a structure of organizing and/or representing the individual conditions subject to the analysis. In some embodiments, the collapsing instructions may be based at least on the one or more system components. For example, the collapsing instructions may specify the system components and/or corresponding conditions that are subject to the analysis. In some embodiments, the collapsing instructions may be based at least on the system classes. For example, a particular system class and/or corresponding system level effect may be subject to the analysis and the collapsing instructions may specify the particular system class and/or the corresponding system level effect to be analyzed.
In some embodiments, the collapsing instructions may be obtained from a user. For example, the user may specify the system components, the system level effects, and/or the individual conditions subject to the analysis. In some embodiments, the collapsing instructions may be generated by the system and/or a remote system. For example, the system may generate the collapsing instructions based at least on architectural design of the system. For instance, the system may generate an example and/or a recommended collapsing instructions that may be suitable for the analysis being performed. In some embodiments, the collapsing instructions may combine the recommended collapsing instructions by the system and the collapsing instructions determined by the user. For instance, the user may modify the recommended collapsing instructions to better suit needs of the user.
At block 312, a visualization within a graphical user interface (GUI) may be generated. In some embodiments, the visualization may include the second set of conditions including at least the subset of conditions. For example, the subset of conditions may be loaded and/or visualized to be more easily accessible for the analysis. In some embodiments, the second set of conditions and/or the subset of conditions may be visualized based at least on the collapsing criteria. For instance, the second set of conditions and/or the subset of conditions may be visualized following the structure specified in the collapsing criteria.
At block 314, the visualization within the GUI may be displayed on a display screen. In some embodiments, the display may allow the visualization to be interactable. For example, the user may interact with the visualization to modify the second set of conditions and/or the subset of conditions. For instance, the user may add, remove, and/or modify the system components and/or the individual conditions corresponding to the system components. Additionally or alternatively, safety mechanisms corresponding to the system components and/or the individual conditions may be added, removed, and/or modified. For example, a mechanism corresponding to a newly added condition may also be added to the visualization.
Modifications, additions, or omissions may be made to the method 300 without departing from the scope of the present disclosure. For example, the operations of method 300 may be implemented in differing order. Additionally or alternatively, two or more operations may be performed at the same time. Furthermore, the outlined operations and actions are only provided as examples, and some of the operations and actions may be optional, combined into fewer operations and actions, or expanded into additional operations and actions without detracting from the essence of the described embodiments.
For example, in some embodiments, the method 300 may further include rolling back and/or applying any changes or modifications made to the system components, the corresponding individual conditions, and/or the safety mechanisms of the second set of conditions to the first set of conditions. For instance, in response to the modifications being made to the system components, the corresponding individual conditions, and/or the safety mechanisms (e.g., based at least on the user inputs obtained through the GUI), the first set of conditions may be updated accordingly.
The vehicle 400 may include components such as a chassis, a vehicle body, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles, and other components of a vehicle. The vehicle 400 may include a propulsion system 450, such as an internal combustion engine, hybrid electric power plant, an all-electric engine, and/or another propulsion system type. The propulsion system 450 may be connected to a drive train of the vehicle 400, which may include a transmission, to enable the propulsion of the vehicle 400. The propulsion system 450 may be controlled in response to receiving signals from the throttle/accelerator 452.
A steering system 454, which may include a steering wheel, may be used to steer the vehicle 400 (e.g., along a desired path or route) when the propulsion system 450 is operating (e.g., when the vehicle is in motion). The steering system 454 may receive signals from a steering actuator 456. The steering wheel may be optional for full automation (Level 5) functionality.
The brake sensor system 446 may be used to operate the vehicle brakes in response to receiving signals from the brake actuators 448 and/or brake sensors.
Controller(s) 436, which may include one or more CPU(s), system on chips (SoCs) 404 (
The controller(s) 436 may provide the signals for controlling one or more components and/or systems of the vehicle 400 in response to sensor data received from one or more sensors (e.g., sensor inputs). The sensor data may be received from, for example and without limitation, global navigation satellite systems sensor(s) 458 (e.g., Global Positioning System sensor(s)), RADAR sensor(s) 460, ultrasonic sensor(s) 462, LIDAR sensor(s) 464, inertial measurement unit (IMU) sensor(s) 466 (e.g., accelerometer(s), gyroscope(s), magnetic compass(es), magnetometer(s), etc.), microphone(s) 496, stereo camera(s) 468, wide-view camera(s) 470 (e.g., fisheye cameras), infrared camera(s) 472, surround camera(s) 474 (e.g., 360 degree cameras), long-range and/or mid-range camera(s) 498, speed sensor(s) 444 (e.g., for measuring the speed of the vehicle 400), vibration sensor(s) 442, steering sensor(s) 440, brake sensor(s) 446 (e.g., as part of the brake sensor system 446), and/or other sensor types.
One or more of the controller(s) 436 may receive inputs (e.g., represented by input data) from an instrument cluster 432 of the vehicle 400 and provide outputs (e.g., represented by output data, display data, etc.) via a human-machine interface (HMI) display 434, an audible annunciator, a loudspeaker, and/or via other components of the vehicle 400. The outputs may include information such as vehicle velocity, speed, time, map data (e.g., the HD map 422 of
The vehicle 400 further includes a network interface 424, which may use one or more wireless antenna(s) 426 and/or modem(s) to communicate over one or more networks. For example, the network interface 424 may be capable of communication over LTE, WCDMA, UMTS, GSM, CDMA2000, etc. The wireless antenna(s) 426 may also enable communication between objects in the environment (e.g., vehicles, mobile devices, etc.), using local area network(s), such as Bluetooth, Bluetooth LE, Z-Wave, ZigBee, etc., and/or low power wide-area network(s) (LPWANs), such as LoRaWAN, SigFox, etc.
The camera types for the cameras may include, but are not limited to, digital cameras that may be adapted for use with the components and/or systems of the vehicle 400. The camera(s) may operate at automotive safety integrity level (ASIL) B and/or at another ASIL. The camera types may be capable of any image capture rate, such as 60 frames per second (fps), 120fps, 240 fps, etc., depending on the embodiment. The cameras may be capable of using rolling shutters, global shutters, another type of shutter, or a combination thereof. In some examples, the color filter array may include a red clear clear clear (RCCC) color filter array, a red clear clear blue (RCCB) color filter array, a red, blue, green clear (RBGC) color filter array, a Foveon X3 color filter array, a Bayer sensors (RGGB) color filter array, a monochrome sensor color filter array, and/or another type of color filter array. In some embodiments, clear pixel cameras, such as cameras with an RCCC, an RCCB, and/or an RBGC color filter array, may be used in an effort to increase light sensitivity.
In some examples, one or more of the camera(s) may be used to perform advanced driver assistance systems (ADAS) functions (e.g., as part of a redundant or fail-safe design). For example, a Multi-Function Mono Camera may be installed to provide functions including lane departure warning, traffic sign assist and intelligent headlamp control. One or more of the camera(s) (e.g., all of the cameras) may record and provide image data (e.g., video) simultaneously.
One or more of the cameras may be mounted in a mounting assembly, such as a custom-designed (3-D printed) assembly, in order to cut out stray light and reflections from within the car (e.g., reflections from the dashboard reflected in the windshield mirrors) which may interfere with the camera's image data capture abilities. With reference to wing-mirror mounting assemblies, the wing-mirror assemblies may be custom 3-D printed so that the camera mounting plate matches the shape of the wing-mirror. In some examples, the camera(s) may be integrated into the wing-mirror. For side-view cameras, the camera(s) may also be integrated within the four pillars at each corner of the cabin.
Cameras with a field of view that include portions of the environment in front of the vehicle 400 (e.g., front-facing cameras) may be used for surround view, to help identify forward-facing paths and obstacles, as well aid in, with the help of one or more controllers 436 and/or control SoCs, providing information critical to generating an occupancy grid and/or determining the preferred vehicle paths. Front-facing cameras may be used to perform many of the same ADAS functions as LIDAR, including emergency braking, pedestrian detection, and collision avoidance. Front-facing cameras may also be used for ADAS functions and systems including Lane Departure Warnings (LDW), Autonomous Cruise Control (ACC), and/or other functions such as traffic sign recognition.
A variety of cameras may be used in a front-facing configuration, including, for example, a monocular camera platform that includes a CMOS (complementary metal oxide semiconductor) color imager. Another example may be a wide-view camera(s) 470 that may be used to perceive objects coming into view from the periphery (e.g., pedestrians, crossing traffic or bicycles). Although only one wide-view camera is illustrated in
One or more stereo cameras 468 may also be included in a front-facing configuration. The stereo camera(s) 468 may include an integrated control unit comprising a scalable processing unit, which may provide a programmable logic (FPGA) and a multi-core micro-processor with an integrated CAN or Ethernet interface on a single chip. Such a unit may be used to generate a 3-D map of the vehicle's environment, including a distance estimate for all the points in the image. An alternative stereo camera(s) 468 may include a compact stereo vision sensor(s) that may include two camera lenses (one each on the left and right) and an image processing chip that may measure the distance from the vehicle to the target object and use the generated information (e.g., metadata) to activate the autonomous emergency braking and lane departure warning functions. Other types of stereo camera(s) 468 may be used in addition to, or alternatively from, those described herein.
Cameras with a field of view that include portions of the environment to the side of the vehicle 400 (e.g., side-view cameras) may be used for surround view, providing information used to create and update the occupancy grid, as well as to generate side impact collision warnings. For example, surround camera(s) 474 (e.g., four surround cameras 474 as illustrated in
Cameras with a field of view that include portions of the environment to the rear of the vehicle 400 (e.g., rear-view cameras) may be used for park assistance, surround view, rear collision warnings, and creating and updating the occupancy grid. A wide variety of cameras may be used including, but not limited to, cameras that are also suitable as a front-facing camera(s) (e.g., long-range and/or mid-range camera(s) 498, stereo camera(s) 468), infrared camera(s) 472, etc.), as described herein.
Each of the components, features, and systems of the vehicle 400 in
Although the bus 402 is described herein as being a CAN bus, this is not intended to be limiting. For example, in addition to, or alternatively from, the CAN bus, FlexRay and/or Ethernet may be used. Additionally, although a single line is used to represent the bus 402, this is not intended to be limiting. For example, there may be any number of busses 402, which may include one or more CAN busses, one or more FlexRay busses, one or more Ethernet busses, and/or one or more other types of busses using a different protocol. In some examples, two or more busses 402 may be used to perform different functions, and/or may be used for redundancy. For example, a first bus 402 may be used for collision avoidance functionality and a second bus 402 may be used for actuation control. In any example, each bus 402 may communicate with any of the components of the vehicle 400, and two or more busses 402 may communicate with the same components. In some examples, each SoC 404, each controller 436, and/or each computer within the vehicle may have access to the same input data (e.g., inputs from sensors of the vehicle 400), and may be connected to a common bus, such the CAN bus.
The vehicle 400 may include one or more controller(s) 436, such as those described herein with respect to
The vehicle 400 may include a system(s) on a chip (SoC) 404. The SoC 404 may include CPU(s) 406, GPU(s) 408, processor(s) 410, cache(s) 412, accelerator(s) 414, data store(s) 416, and/or other components and features not illustrated. The SoC(s) 404 may be used to control the vehicle 400 in a variety of platforms and systems. For example, the SoC(s) 404 may be combined in a system (e.g., the system of the vehicle 400) with an HD map 422 which may obtain map refreshes and/or updates via a network interface 424 from one or more servers (e.g., server(s) 478 of
The CPU(s) 406 may include a CPU cluster or CPU complex (alternatively referred to herein as a “CCPLEX”). The CPU(s) 406 may include multiple cores and/or L2 caches. For example, in some embodiments, the CPU(s) 406 may include eight cores in a coherent multi-processor configuration. In some embodiments, the CPU(s) 406 may include four dual-core clusters where each cluster has a dedicated L2 cache (e.g., a 2 MB L2 cache). The CPU(s) 406 (e.g., the CCPLEX) may be configured to support simultaneous cluster operation enabling any combination of the clusters of the CPU(s) 406 to be active at any given time.
The CPU(s) 406 may implement power management capabilities that include one or more of the following features: individual hardware blocks may be clock-gated automatically when idle to save dynamic power; each core clock may be gated when the core is not actively executing instructions due to execution of WFI/WFE instructions; each core may be independently power-gated; each core cluster may be independently clock-gated when all cores are clock-gated or power-gated; and/or each core cluster may be independently power-gated when all cores are power-gated. The CPU(s) 406 may further implement an enhanced algorithm for managing power states, where allowed power states and expected wakeup times are specified, and the hardware/microcode determines the best power state to enter for the core, cluster, and CCPLEX. The processing cores may support simplified power state entry sequences in software with the work offloaded to microcode.
The GPU(s) 408 may include an integrated GPU (alternatively referred to herein as an “iGPU”). The GPU(s) 408 may be programmable and may be efficient for parallel workloads. The GPU(s) 408, in some examples, may use an enhanced tensor instruction set. The GPU(s) 408 may include one or more streaming microprocessors, where each streaming microprocessor may include an L1 cache (e.g., an L1 cache with at least 96 KB storage capacity), and two or more of the streaming microprocessors may share an L2 cache (e.g., an L2 cache with a 512 KB storage capacity). In some embodiments, the GPU(s) 408 may include at least eight streaming microprocessors. The GPU(s) 408 may use compute application programming interface(s) (API(s)). In addition, the GPU(s) 408 may use one or more parallel computing platforms and/or programming models (e.g., NVIDIA's CUDA).
The GPU(s) 408 may be power-optimized for best performance in automotive and embedded use cases. For example, the GPU(s) 408 may be fabricated on a Fin field-effect transistor (FinFET). However, this is not intended to be limiting, and the GPU(s) 408 may be fabricated using other semiconductor manufacturing processes. Each streaming microprocessor may incorporate a number of mixed-precision processing cores partitioned into multiple blocks. For example, and without limitation, 64 PF32 cores and 32 PF64 cores may be partitioned into four processing blocks. In such an example, each processing block may be allocated 16 FP32cores, 8 FP64 cores, 16 INT32 cores, two mixed-precision NVIDIA TENSOR COREs for deep learning matrix arithmetic, an L0 instruction cache, a warp scheduler, a dispatch unit, and/or a 64 KB register file. In addition, the streaming microprocessors may include independent parallel integer and floating-point data paths to provide for efficient execution of workloads with a mix of computation and addressing calculations. The streaming microprocessors may include independent thread-scheduling capability to enable finer-grain synchronization and cooperation between parallel threads. The streaming microprocessors may include a combined L1 data cache and shared memory unit in order to improve performance while simplifying programming.
The GPU(s) 408 may include a high bandwidth memory (HBM) and/or a 16 GB HBM2 memory subsystem to provide, in some examples, about 900 GB/second peak memory bandwidth. In some examples, in addition to, or alternatively from, the HBM memory, a synchronous graphics random-access memory (SGRAM) may be used, such as a graphics double data rate type five synchronous random-access memory (GDDR5).
The GPU(s) 408 may include unified memory technology including access counters to allow for more accurate migration of memory pages to the processor that accesses them most frequently, thereby improving efficiency for memory ranges shared between processors. In some examples, address translation services (ATS) support may be used to allow the GPU(s) 408 to access the CPU(s) 406 page tables directly. In such examples, when the GPU(s) 408 memory management unit (MMU) experiences a miss, an address translation request may be transmitted to the CPU(s) 406. In response, the CPU(s) 406 may look in its page tables for the virtual-to-physical mapping for the address and transmits the translation back to the GPU(s) 408. As such, unified memory technology may allow a single unified virtual address space for memory of both the CPU(s) 406 and the GPU(s) 408, thereby simplifying the GPU(s) 408 programming and porting of applications to the GPU(s) 408.
In addition, the GPU(s) 408 may include an access counter that may keep track of the frequency of access of the GPU(s) 408 to memory of other processors. The access counter may help ensure that memory pages are moved to the physical memory of the processor that is accessing the pages most frequently.
The SoC(s) 404 may include any number of cache(s) 412, including those described herein. For example, the cache(s) 412 may include an L3 cache that is available to both the CPU(s) 406 and the GPU(s) 408 (e.g., that is connected to both the CPU(s) 406 and the GPU(s) 408). The cache(s) 412 may include a write-back cache that may keep track of states of lines, such as by using a cache coherence protocol (e.g., MEI, MESI, MSI, etc.). The L3 cache may include 4 MB or more, depending on the embodiment, although smaller cache sizes may be used.
The SoC(s) 404 may include an arithmetic logic unit(s) (ALU(s)) which may be leveraged in performing processing with respect to any of the variety of tasks or operations of the vehicle 400—such as processing DNNs. In addition, the SoC(s) 404 may include a floating point unit(s) (FPU(s))—or other math coprocessor or numeric coprocessor types—for performing mathematical operations within the system. For example, the SoC(s) 404 may include one or more FPUs integrated as execution units within a CPU(s) 406 and/or GPU(s) 408.
The SoC(s) 404 may include one or more accelerators 414 (e.g., hardware accelerators, software accelerators, or a combination thereof). For example, the SoC(s) 404 may include a hardware acceleration cluster that may include optimized hardware accelerators and/or large on-chip memory. The large on-chip memory (e.g., 4 MB of SRAM), may enable the hardware acceleration cluster to accelerate neural networks and other calculations. The hardware acceleration cluster may be used to complement the GPU(s) 408 and to off-load some of the tasks of the GPU(s) 408 (e.g., to free up more cycles of the GPU(s) 408 for performing other tasks). As an example, the accelerator(s) 414 may be used for targeted workloads (e.g., perception, convolutional neural networks (CNNs), etc.) that are stable enough to be amenable to acceleration. The term “CNN,” as used herein, may include all types of CNNs, including region-based or regional convolutional neural networks (RCNNs) and Fast RCNNs (e.g., as used for object detection).
The accelerator(s) 414 (e.g., the hardware acceleration cluster) may include a deep learning accelerator(s) (DLA). The DLA(s) may include one or more Tensor processing units (TPUs) that may be configured to provide an additional ten trillion operations per second for deep learning applications and inferencing. The TPUs may be accelerators configured to, and optimized for, performing image processing functions (e.g., for CNNs, RCNNs, etc.). The DLA(s) may further be optimized for a specific set of neural network types and floating point operations, as well as inferencing. The design of the DLA(s) may provide more performance per millimeter than a general-purpose GPU, and vastly exceeds the performance of a CPU. The TPU(s) may perform several functions, including a single-instance convolution function, supporting, for example, INT8, INT16, and FP16 data types for both features and weights, as well as post-processor functions.
The DLA(s) may quickly and efficiently execute neural networks, especially CNNs, on processed or unprocessed data for any of a variety of functions, including, for example and without limitation: a CNN for object identification and detection using data from camera sensors; a CNN for distance estimation using data from camera sensors; a CNN for emergency vehicle detection and identification and detection using data from microphones; a CNN for facial recognition and vehicle owner identification using data from camera sensors; and/or a CNN for security and/or safety related events.
The DLA(s) may perform any function of the GPU(s) 408, and by using an inference accelerator, for example, a designer may target either the DLA(s) or the GPU(s) 408 for any function. For example, the designer may focus processing of CNNs and floating point operations on the DLA(s) and leave other functions to the GPU(s) 408 and/or other accelerator(s) 414.
The accelerator(s) 414 (e.g., the hardware acceleration cluster) may include a programmable vision accelerator(s) (PVA), which may alternatively be referred to herein as a computer vision accelerator. The PVA(s) may be designed and configured to accelerate computer vision algorithms for the advanced driver assistance systems (ADAS), autonomous driving, and/or augmented reality (AR) and/or virtual reality (VR) applications. The PVA(s) may provide a balance between performance and flexibility. For example, each PVA(s) may include, for example and without limitation, any number of reduced instruction set computer (RISC) cores, direct memory access (DMA), and/or any number of vector processors.
The RISC cores may interact with image sensors (e.g., the image sensors of any of the cameras described herein), image signal processor(s), and/or the like. Each of the RISC cores may include any amount of memory. The RISC cores may use any of a number of protocols, depending on the embodiment. In some examples, the RISC cores may execute a real-time operating system (RTOS). The RISC cores may be implemented using one or more integrated circuit devices, application specific integrated circuits (ASICs), and/or memory devices. For example, the RISC cores may include an instruction cache and/or a tightly coupled RAM.
The DMA may enable components of the PVA(s) to access the system memory independently of the CPU(s) 406. The DMA may support any number of features used to provide optimization to the PVA including, but not limited to, supporting multi-dimensional addressing and/or circular addressing. In some examples, the DMA may support up to six or more dimensions of addressing, which may include block width, block height, block depth, horizontal block stepping, vertical block stepping, and/or depth stepping.
The vector processors may be programmable processors that may be designed to efficiently and flexibly execute programming for computer vision algorithms and provide signal processing capabilities. In some examples, the PVA may include a PVA core and two vector processing subsystem partitions. The PVA core may include a processor subsystem, DMA engine(s) (e.g., two DMA engines), and/or other peripherals. The vector processing subsystem may operate as the primary processing engine of the PVA, and may include a vector processing unit (VPU), an instruction cache, and/or vector memory (e.g., VMEM). A VPU core may include a digital signal processor such as, for example, a single instruction, multiple data (SIMD), very long instruction word (VLIW) digital signal processor. The combination of the SIMD and VLIW may enhance throughput and speed.
Each of the vector processors may include an instruction cache and may be coupled to dedicated memory. As a result, in some examples, each of the vector processors may be configured to execute independently of the other vector processors. In other examples, the vector processors that are included in a particular PVA may be configured to employ data parallelism. For example, in some embodiments, the plurality of vector processors included in a single PVA may execute the same computer vision algorithm, but on different regions of an image. In other examples, the vector processors included in a particular PVA may simultaneously execute different computer vision algorithms, on the same image, or even execute different algorithms on sequential images or portions of an image. Among other things, any number of PVAs may be included in the hardware acceleration cluster and any number of vector processors may be included in each of the PVAs. In addition, the PVA(s) may include additional error correcting code (ECC) memory, to enhance overall system safety.
The accelerator(s) 414 (e.g., the hardware acceleration cluster) may include a computer vision network on-chip and SRAM, for providing a high-bandwidth, low latency SRAM for the accelerator(s) 414. In some examples, the on-chip memory may include at least 4 MB SRAM, consisting of, for example and without limitation, eight field-configurable memory blocks, that may be accessible by both the PVA and the DLA. Each pair of memory blocks may include an advanced peripheral bus (APB) interface, configuration circuitry, a controller, and a multiplexer. Any type of memory may be used. The PVA and DLA may access the memory via a backbone that provides the PVA and DLA with high-speed access to memory. The backbone may include a computer vision network on-chip that interconnects the PVA and the DLA to the memory (e.g., using the APB).
The computer vision network on-chip may include an interface that determines, before transmission of any control signal/address/data, that both the PVA and the DLA provide ready and valid signals. Such an interface may provide for separate phases and separate channels for transmitting control signals/addresses/data, as well as burst-type communications for continuous data transfer. This type of interface may comply with ISO 26262 or IEC 61508 standards, although other standards and protocols may be used.
In some examples, the SoC(s) 404 may include a real-time ray-tracing hardware accelerator, such as described in U.S. patent application Ser. No. 16/101,232, filed on Aug. 10, 2018. The real-time ray-tracing hardware accelerator may be used to quickly and efficiently determine the positions and extents of objects (e.g., within a world model), to generate real-time visualization simulations, for RADAR signal interpretation, for sound propagation synthesis and/or analysis, for simulation of SONAR systems, for general wave propagation simulation, for comparison to LIDAR data for purposes of localization and/or other functions, and/or for other uses. In some embodiments, one or more tree traversal units (TTUs) may be used for executing one or more ray-tracing related operations.
The accelerator(s) 414 (e.g., the hardware accelerator cluster) have a wide array of uses for autonomous driving. The PVA may be a programmable vision accelerator that may be used for key processing stages in ADAS and autonomous vehicles. The PVA's capabilities are a good match for algorithmic domains needing predictable processing, at low power and low latency. In other words, the PVA performs well on semi-dense or dense regular computation, even on small data sets, which need predictable run-times with low latency and low power. Thus, in the context of platforms for autonomous vehicles, the PVAs are designed to run classic computer vision algorithms, as they are efficient at object detection and operating on integer math.
For example, according to one embodiment of the technology, the PVA is used to perform computer stereo vision. A semi-global matching-based algorithm may be used in some examples, although this is not intended to be limiting. Many applications for Level 3-5 autonomous driving require motion estimation/stereo matching on-the-fly (e.g., structure from motion, pedestrian recognition, lane detection, etc.). The PVA may perform computer stereo vision function on inputs from two monocular cameras.
In some examples, the PVA may be used to perform dense optical flow. According to process raw RADAR data (e.g., using a 4D Fast Fourier Transform) to processed RADAR. In other examples, the PVA is used for time of flight depth processing, by processing raw time of flight data to provide processed time of flight data, for example.
The DLA may be used to run any type of network to enhance control and driving safety, including, for example, a neural network that outputs a measure of confidence for each object detection. Such a confidence value may be interpreted as a probability, or as providing a relative “weight” of each detection compared to other detections. This confidence value enables the system to make further decisions regarding which detections should be considered as true positive detections rather than false positive detections. For example, the system may set a threshold value for the confidence and consider only the detections exceeding the threshold value as true positive detections. In an automatic emergency braking (AEB) system, false positive detections would cause the vehicle to automatically perform emergency braking, which is obviously undesirable. Therefore, only the most confident detections should be considered as triggers for AEB. The DLA may run a neural network for regressing the confidence value. The neural network may take as its input at least some subset of parameters, such as bounding box dimensions, ground plane estimate obtained (e.g. from another subsystem), inertial measurement unit (IMU) sensor 466 output that correlates with the vehicle 400 orientation, distance, 3D location estimates of the object obtained from the neural network and/or other sensors (e.g., LIDAR sensor(s) 464 or RADAR sensor(s) 460), among others.
The SoC(s) 404 may include data store(s) 416 (e.g., memory). The data store(s) 416 may be on-chip memory of the SoC(s) 404, which may store neural networks to be executed on the GPU and/or the DLA. In some examples, the data store(s) 416 may be large enough in capacity to store multiple instances of neural networks for redundancy and safety. The data store(s) 416 may comprise L2 or L3 cache(s) 412. Reference to the data store(s) 416 may include reference to the memory associated with the PVA, DLA, and/or other accelerator(s) 414, as described herein.
The SoC(s) 404 may include one or more processor(s) 410 (e.g., embedded processors). The processor(s) 410 may include a boot and power management processor that may be a dedicated processor and subsystem to handle boot power and management functions and related security enforcement. The boot and power management processor may be a part of the SoC(s) 404 boot sequence and may provide runtime power management services. The boot power and management processor may provide clock and voltage programming, assistance in system low power state transitions, management of SoC(s) 404 thermals and temperature sensors, and/or management of the SoC(s) 404 power states. Each temperature sensor may be implemented as a ring-oscillator whose output frequency is proportional to temperature, and the SoC(s) 404 may use the ring-oscillators to detect temperatures of the CPU(s) 406, GPU(s) 408, and/or accelerator(s) 414. If temperatures are determined to exceed a threshold, the boot and power management processor may enter a temperature fault routine and put the SoC(s) 404 into a lower power state and/or put the vehicle 400 into a chauffeur to safe-stop mode (e.g., bring the vehicle 400 to a safe stop).
The processor(s) 410 may further include a set of embedded processors that may serve as an audio processing engine. The audio processing engine may be an audio subsystem that enables full hardware support for multi-channel audio over multiple interfaces, and a broad and flexible range of audio I/O interfaces. In some examples, the audio processing engine is a dedicated processor core with a digital signal processor with dedicated RAM.
The processor(s) 410 may further include an always-on processor engine that may provide necessary hardware features to support low power sensor management and wake use cases. The always-on processor engine may include a processor core, a tightly coupled RAM, supporting peripherals (e.g., timers and interrupt controllers), various I/O controller peripherals, and routing logic.
The processor(s) 410 may further include a safety cluster engine that includes a dedicated processor subsystem to handle safety management for automotive applications. The safety cluster engine may include two or more processor cores, a tightly coupled RAM, support peripherals (e.g., timers, an interrupt controller, etc.), and/or routing logic. In a safety mode, the two or more cores may operate in a lockstep mode and function as a single core with comparison logic to detect any differences between their operations.
The processor(s) 410 may further include a real-time camera engine that may include a dedicated processor subsystem for handling real-time camera management.
The processor(s) 410 may further include a high dynamic range signal processor that may include an image signal processor that is a hardware engine that is part of the camera processing pipeline.
The processor(s) 410 may include a video image compositor that may be a processing block (e.g., implemented on a microprocessor) that implements video post-processing functions needed by a video playback application to produce the final image for the player window. The video image compositor may perform lens distortion correction on wide-view camera(s) 470, surround camera(s) 474, and/or on in-cabin monitoring camera sensors. An in-cabin monitoring camera sensor is preferably monitored by a neural network running on another instance of the Advanced SoC, configured to identify in-cabin events and respond accordingly. In-cabin system may perform lip reading to activate cellular service and place a phone call, dictate emails, change the vehicle's destination, activate or change the vehicle's infotainment system and settings, or provide voice-activated web surfing. Certain functions are available to the driver only when the vehicle is operating in an autonomous mode, and are disabled otherwise.
The video image compositor may include enhanced temporal noise reduction for both spatial and temporal noise reduction. For example, where motion occurs in a video, the noise reduction weights spatial information appropriately, decreasing the weight of information provided by adjacent frames. Where an image or portion of an image does not include motion, the temporal noise reduction performed by the video image compositor may use information from the previous image to reduce noise in the current image.
The video image compositor may also be configured to perform stereo rectification on input stereo lens frames. The video image compositor may further be used for user interface composition when the operating system desktop is in use, and the GPU(s) 408 is not required to continuously render new surfaces. Even when the GPU(s) 408 is powered on and active doing 3D rendering, the video image compositor may be used to offload the GPU(s) 408 to improve performance and responsiveness.
The SoC(s) 404 may further include a mobile industry processor interface (MIPI) camera serial interface for receiving video and input from cameras, a high-speed interface, and/or a video input block that may be used for camera and related pixel input functions. The SoC(s) 404 may further include an input/output controller(s) that may be controlled by software and may be used for receiving I/O signals that are uncommitted to a specific role.
The SoC(s) 404 may further include a broad range of peripheral interfaces to enable communication with peripherals, audio codecs, power management, and/or other devices. The SoC(s) 404 may be used to process data from cameras (e.g., connected over Gigabit Multimedia Serial Link and Ethernet), sensors (e.g., LIDAR sensor(s) 464, RADAR sensor(s) 460, etc. that may be connected over Ethernet), data from bus 402 (e.g., speed of vehicle 400, steering wheel position, etc.), data from GNSS sensor(s) 458 (e.g., connected over Ethernet or CAN bus). The SoC(s) 404 may further include dedicated high-performance mass storage controllers that may include their own DMA engines, and that may be used to free the CPU(s) 406 from routine data management tasks.
The SoC(s) 404 may be an end-to-end platform with a flexible architecture that spans automation levels 3-5, thereby providing a comprehensive functional safety architecture that leverages and makes efficient use of computer vision and ADAS techniques for diversity and redundancy, provides a platform for a flexible, reliable driving software stack, along with deep learning tools. The SoC(s) 404 may be faster, more reliable, and even more energy-efficient and space-efficient than conventional systems. For example, the accelerator(s) 414, when combined with the CPU(s) 406, the GPU(s) 408, and the data store(s) 416, may provide for a fast, efficient platform for level 3-5 autonomous vehicles.
The technology thus provides capabilities and functionality that cannot be achieved by conventional systems. For example, computer vision algorithms may be executed on CPUs, which may be configured using high-level programming language, such as the C programming language, to execute a wide variety of processing algorithms across a wide variety of visual data. However, CPUs are oftentimes unable to meet the performance requirements of many computer vision applications, such as those related to execution time and power consumption, for example. In particular, many CPUs are unable to execute complex object detection algorithms in real-time, which is a requirement of in-vehicle ADAS applications, and a requirement for practical Level 3-5 autonomous vehicles.
In contrast to conventional systems, by providing a CPU complex, GPU complex, and a hardware acceleration cluster, the technology described herein allows for multiple neural networks to be performed simultaneously and/or sequentially, and for the results to be combined together to enable Level 3-5 autonomous driving functionality. For example, a CNN executing on the DLA or dGPU (e.g., the GPU(s) 420) may include a text and word recognition, allowing the supercomputer to read and understand traffic signs, including signs for which the neural network has not been specifically trained. The DLA may further include a neural network that is able to identify, interpret, and provides semantic understanding of the sign, and to pass that semantic understanding to the path-planning modules running on the CPU Complex.
As another example, multiple neural networks may be run simultaneously, as is required for Level 3, 4, or 5 driving. For example, a warning sign consisting of “Caution: flashing lights indicate icy conditions,” along with an electric light, may be independently or collectively interpreted by several neural networks. The sign itself may be identified as a traffic sign by a first deployed neural network (e.g., a neural network that has been trained), the text “Flashing lights indicate icy conditions” may be interpreted by a second deployed neural network, which informs the vehicle's path-planning software (preferably executing on the CPU Complex) that when flashing lights are detected, icy conditions exist. The flashing light may be identified by operating a third deployed neural network over multiple frames, informing the vehicle's path-planning software of the presence (or absence) of flashing lights. All three neural networks may run simultaneously, such as within the DLA and/or on the GPU(s) 408.
In some examples, a CNN for facial recognition and vehicle owner identification may use data from camera sensors to identify the presence of an authorized driver and/or owner of the vehicle 400. The always-on sensor processing engine may be used to unlock the vehicle when the owner approaches the driver door and turn on the lights, and, in security mode, to disable the vehicle when the owner leaves the vehicle. In this way, the SoC(s) 404 provide for security against theft and/or carjacking.
In another example, a CNN for emergency vehicle detection and identification may use data from microphones 496 to detect and identify emergency vehicle sirens. In contrast to conventional systems, that use general classifiers to detect sirens and manually extract features, the SoC(s) 404 use the CNN for classifying environmental and urban sounds, as well as classifying visual data. In a preferred embodiment, the CNN running on the DLA is trained to identify the relative closing speed of the emergency vehicle (e.g., by using the Doppler Effect). The CNN may also be trained to identify emergency vehicles specific to the local area in which the vehicle is operating, as identified by GNSS sensor(s) 458. Thus, for example, when operating in Europe the CNN will seek to detect European sirens, and when in the United States the CNN will seek to identify only North American sirens. Once an emergency vehicle is detected, a control program may be used to execute an emergency vehicle safety routine, slowing the vehicle, pulling over to the side of the road, parking the vehicle, and/or idling the vehicle, with the assistance of ultrasonic sensors 462, until the emergency vehicle(s) passes.
The vehicle may include a CPU(s) 418 (e.g., discrete CPU(s), or dCPU(s)), that may be coupled to the SoC(s) 404 via a high-speed interconnect (e.g., PCIe). The CPU(s) 418 may include an X86 processor, for example. The CPU(s) 418 may be used to perform any of a variety of functions, including arbitrating potentially inconsistent results between ADAS sensors and the SoC(s) 404, and/or monitoring the status and health of the controller(s) 436 and/or infotainment SoC 430, for example.
The vehicle 400 may include a GPU(s) 420 (e.g., discrete GPU(s), or dGPU(s)), that may be coupled to the SoC(s) 404 via a high-speed interconnect (e.g., NVIDIA's NVLINK). The GPU(s) 420 may provide additional artificial intelligence functionality, such as by executing redundant and/or different neural networks, and may be used to train and/or update neural networks based on input (e.g., sensor data) from sensors of the vehicle 400.
The vehicle 400 may further include the network interface 424 which may include one or more wireless antennas 426 (e.g., one or more wireless antennas for different communication protocols, such as a cellular antenna, a Bluetooth antenna, etc.). The network interface 424 may be used to enable wireless connectivity over the Internet with the cloud (e.g., with the server(s) 478 and/or other network devices), with other vehicles, and/or with computing devices (e.g., client devices of passengers). To communicate with other vehicles, a direct link may be established between the two vehicles and/or an indirect link may be established (e.g., across networks and over the Internet). Direct links may be provided using a vehicle-to-vehicle communication link. The vehicle-to-vehicle communication link may provide the vehicle 400 information about vehicles in proximity to the vehicle 400 (e.g., vehicles in front of, on the side of, and/or behind the vehicle 400). This functionality may be part of a cooperative adaptive cruise control functionality of the vehicle 400.
The network interface 424 may include a SoC that provides modulation and demodulation functionality and enables the controller(s) 436 to communicate over wireless networks. The network interface 424 may include a radio frequency front-end for up-conversion from baseband to radio frequency, and down conversion from radio frequency to baseband. The frequency conversions may be performed through well-known processes, and/or may be performed using super-heterodyne processes. In some examples, the radio frequency front end functionality may be provided by a separate chip. The network interface may include wireless functionality for communicating over LTE, WCDMA, UMTS, GSM, CDMA2000, Bluetooth, Bluetooth LE, Wi-Fi, Z-Wave, ZigBee, LoRaWAN, and/or other wireless protocols.
The vehicle 400 may further include data store(s) 428, which may include off-chip (e.g., off the SoC(s) 404) storage. The data store(s) 428 may include one or more storage elements including RAM, SRAM, DRAM, VRAM, Flash, hard disks, and/or other components and/or devices that may store at least one bit of data.
The vehicle 400 may further include GNSS sensor(s) 458. The GNSS sensor(s) 458 (e.g., GPS, assisted GPS sensors, differential GPD (DGPS) sensors, etc.), to assist in mapping, perception, occupancy grid generation, and/or path planning functions. Any number of GNSS sensor(s) 458 may be used, including, for example and without limitation, a GPS using a USB connector with an Ethernet to Serial (RS-232) bridge.
The vehicle 400 may further include RADAR sensor(s) 460. The RADAR sensor(s) 460 may be used by the vehicle 400 for long-range vehicle detection, even in darkness and/or severe weather conditions. RADAR functional safety levels may be ASIL B. The RADAR sensor(s) 460 may use the CAN and/or the bus 402 (e.g., to transmit data generated by the RADAR sensor(s) 460) for control and to access object tracking data, with access to Ethernet to access raw data, in some examples. A wide variety of RADAR sensor types may be used. For example, and without limitation, the RADAR sensor(s) 460 may be suitable for front, rear, and side RADAR use. In some example, Pulse Doppler RADAR sensor(s) are used.
The RADAR sensor(s) 460 may include different configurations, such as long-range with narrow field of view, short-range with wide field of view, short-range side coverage, etc. In some examples, long-range RADAR may be used for adaptive cruise control functionality. The long-range RADAR systems may provide a broad field of view realized by two or more independent scans, such as within a 250 m range. The RADAR sensor(s) 460 may help in distinguishing between static and moving objects, and may be used by ADAS systems for emergency brake assist and forward collision warning. Long-range RADAR sensors may include monostatic multimodal RADAR with multiple (e.g., six or more) fixed RADAR antennae and a high-speed CAN and FlexRay interface. In an example with six antennae, the central four antennae may create a focused beam pattern, designed to record the vehicle's 400 surrounding at higher speeds with minimal interference from traffic in adjacent lanes. The other two antennae may expand the field of view, making it possible to quickly detect vehicles entering or leaving the vehicle's 400 lane.
Mid-range RADAR systems may include, as an example, a range of up to 160 m (front) or 80 m (rear), and a field of view of up to 42 degrees (front) or 150 degrees (rear). Short-range RADAR systems may include, without limitation, RADAR sensors designed to be installed at both ends of the rear bumper. When installed at both ends of the rear bumper, such a RADAR sensor systems may create two beams that constantly monitor the blind spot in the rear and next to the vehicle.
Short-range RADAR systems may be used in an ADAS system for blind spot detection and/or lane change assist.
The vehicle 400 may further include ultrasonic sensor(s) 462. The ultrasonic sensor(s) 462, which may be positioned at the front, back, and/or the sides of the vehicle 400, may be used for park assist and/or to create and update an occupancy grid. A wide variety of ultrasonic sensor(s) 462 may be used, and different ultrasonic sensor(s) 462 may be used for different ranges of detection (e.g., 2.5 m, 4 m). The ultrasonic sensor(s) 462 may operate at functional safety levels of ASIL B.
The vehicle 400 may include LIDAR sensor(s) 464. The LIDAR sensor(s) 464 may be used for object and pedestrian detection, emergency braking, collision avoidance, and/or other functions. The LIDAR sensor(s) 464 may be functional safety level ASIL B. In some examples, the vehicle 400 may include multiple LIDAR sensors 464 (e.g., two, four, six, etc.) that may use Ethernet (e.g., to provide data to a Gigabit Ethernet switch).
In some examples, the LIDAR sensor(s) 464 may be capable of providing a list of objects and their distances for a 360-degree field of view. Commercially available LIDAR sensor(s) 464 may have an advertised range of approximately 100 m, with an accuracy of 2 cm-3 cm, and with support for a 100 Mbps Ethernet connection, for example. In some examples, one or more non-protruding LIDAR sensors 464 may be used. In such examples, the LIDAR sensor(s) 464 may be implemented as a small device that may be embedded into the front, rear, sides, and/or corners of the vehicle 400. The LIDAR sensor(s) 464, in such examples, may provide up to a 120-degree horizontal and 35-degree vertical field-of-view, with a 200 m range even for low-reflectivity objects. Front-mounted LIDAR sensor(s) 464 may be configured for a horizontal field of view between 45 degrees and 135 degrees.
In some examples, LIDAR technologies, such as 3D flash LIDAR, may also be used. 3D Flash LIDAR uses a flash of a laser as a transmission source, to illuminate vehicle surroundings up to approximately 200 m. A flash LIDAR unit includes a receptor, which records the laser pulse transit time and the reflected light on each pixel, which in turn corresponds to the range from the vehicle to the objects. Flash LIDAR may allow for highly accurate and distortion-free images of the surroundings to be generated with every laser flash. In some examples, four flash LIDAR sensors may be deployed, one at each side of the vehicle 400. Available 3D flash LIDAR systems include a solid-state 3D staring array LIDAR camera with no moving parts other than a fan (e.g., a non-scanning LIDAR device). The flash LIDAR device may use a 5 nanosecond class I (eye-safe) laser pulse per frame and may capture the reflected laser light in the form of 3D range point clouds and co-registered intensity data. By using flash LIDAR, and because flash LIDAR is a solid-state device with no moving parts, the LIDAR sensor(s) 464 may be less susceptible to motion blur, vibration, and/or shock.
The vehicle may further include IMU sensor(s) 466. The IMU sensor(s) 466 may be located at a center of the rear axle of the vehicle 400, in some examples. The IMU sensor(s) 466 may include, for example and without limitation, an accelerometer(s), a magnetometer(s), a gyroscope(s), a magnetic compass(es), and/or other sensor types. In some examples, such as in six-axis applications, the IMU sensor(s) 466 may include accelerometers and gyroscopes, while in nine-axis applications, the IMU sensor(s) 466 may include accelerometers, gyroscopes, and magnetometers.
In some embodiments, the IMU sensor(s) 466 may be implemented as a miniature, high-performance GPS-Aided Inertial Navigation System (GPS/INS) that combines micro-electro-mechanical systems (MEMS) inertial sensors, a high-sensitivity GPS receiver, and advanced Kalman filtering algorithms to provide estimates of position, velocity, and attitude. As such, in some examples, the IMU sensor(s) 466 may enable the vehicle 400 to estimate heading without requiring input from a magnetic sensor by directly observing and correlating the changes in velocity from GPS to the IMU sensor(s) 466. In some examples, the IMU sensor(s) 466 and the GNSS sensor(s) 458 may be combined in a single integrated unit.
The vehicle may include microphone(s) 496 placed in and/or around the vehicle 400. The microphone(s) 496 may be used for emergency vehicle detection and identification, among other things.
The vehicle may further include any number of camera types, including stereo camera(s) 468, wide-view camera(s) 470, infrared camera(s) 472, surround camera(s) 474, long-range and/or mid-range camera(s) 498, and/or other camera types. The cameras may be used to capture image data around an entire periphery of the vehicle 400. The types of cameras used depends on the embodiments and requirements for the vehicle 400, and any combination of camera types may be used to provide the necessary coverage around the vehicle 400. In addition, the number of cameras may differ depending on the embodiment. For example, the vehicle may include six cameras, seven cameras, ten cameras, twelve cameras, and/or another number of cameras. The cameras may support, as an example and without limitation, Gigabit Multimedia Serial Link (GMSL) and/or Gigabit Ethernet. Each of the camera(s) is described with more detail herein with respect to
The vehicle 400 may further include vibration sensor(s) 442. The vibration sensor(s) 442 may measure vibrations of components of the vehicle, such as the axle(s). For example, changes in vibrations may indicate a change in road surfaces. In another example, when two or more vibration sensors 442 are used, the differences between the vibrations may be used to determine friction or slippage of the road surface (e.g., when the difference in vibration is between a power-driven axle and a freely rotating axle).
The vehicle 400 may include an ADAS system 438. The ADAS system 438 may include a SoC, in some examples. The ADAS system 438 may include autonomous/adaptive/automatic cruise control (ACC), cooperative adaptive cruise control (CACC), forward crash warning (FCW), automatic emergency braking (AEB), lane departure warnings (LDW), lane keep assist (LKA), blind spot warning (BSW), rear cross-traffic warning (RCTW), collision warning systems (CWS), lane centering (LC), and/or other features and functionality.
The ACC systems may use RADAR sensor(s) 460, LIDAR sensor(s) 464, and/or a camera(s). The ACC systems may include longitudinal ACC and/or lateral ACC. Longitudinal ACC monitors and controls the distance to the vehicle immediately ahead of the vehicle 400 and automatically adjust the vehicle speed to maintain a safe distance from vehicles ahead. Lateral ACC performs distance keeping, and advises the vehicle 400 to change lanes when necessary. Lateral ACC is related to other ADAS applications such as LCA and CWS.
CACC uses information from other vehicles that may be received via the network interface 424 and/or the wireless antenna(s) 426 from other vehicles via a wireless link, or indirectly, over a network connection (e.g., over the Internet). Direct links may be provided by a vehicle-to-vehicle (V2V) communication link, while indirect links may be infrastructure-to-vehicle (I2V) communication link. In general, the V2V communication concept provides information about the immediately preceding vehicles (e.g., vehicles immediately ahead of and in the same lane as the vehicle 400), while the I2V communication concept provides information about traffic further ahead. CACC systems may include either or both I2V and V2V information sources. Given the information of the vehicles ahead of the vehicle 400, CACC may be more reliable, and it has potential to improve traffic flow smoothness and reduce congestion on the road.
FCW systems are designed to alert the driver to a hazard, so that the driver may take corrective action. FCW systems use a front-facing camera and/or RADAR sensor(s) 460, coupled to a dedicated processor, DSP, FPGA, and/or ASIC, that is electrically coupled to driver feedback, such as a display, speaker, and/or vibrating component. FCW systems may provide a warning, such as in the form of a sound, visual warning, vibration and/or a quick brake pulse.
AEB systems detect an impending forward collision with another vehicle or other object, and may automatically apply the brakes if the driver does not take corrective action within a specified time or distance parameter. AEB systems may use front-facing camera(s) and/or RADAR sensor(s) 460, coupled to a dedicated processor, DSP, FPGA, and/or ASIC. When the AEB system detects a hazard, it typically first alerts the driver to take corrective action to avoid the collision and, if the driver does not take corrective action, the AEB system may automatically apply the brakes in an effort to prevent, or at least mitigate, the impact of the predicted collision. AEB systems, may include techniques such as dynamic brake support and/or crash imminent braking.
LDW systems provide visual, audible, and/or tactile warnings, such as steering wheel or seat vibrations, to alert the driver when the vehicle 400 crosses lane markings. A LDW system does not activate when the driver indicates an intentional lane departure, by activating a turn signal. LDW systems may use front-side facing cameras, coupled to a dedicated processor, DSP, FPGA, and/or ASIC, that is electrically coupled to driver feedback, such as a display, speaker, and/or vibrating component.
LKA systems are a variation of LDW systems. LKA systems provide steering input or braking to correct the vehicle 400 if the vehicle 400 starts to exit the lane. BSW systems detects and warn the driver of vehicles in an automobile's blind spot. BSW systems may provide a visual, audible, and/or tactile alert to indicate that merging or changing lanes is unsafe. The system may provide an additional warning when the driver uses a turn signal. BSW systems may use rear-side facing camera(s) and/or RADAR sensor(s).
RCTW systems may provide visual, audible, and/or tactile notification when an object is detected outside the rear-camera range when the vehicle 400 is backing up. Some RCTW systems include AEB to ensure that the vehicle brakes are applied to avoid a crash. RCTW systems may use one or more rear-facing RADAR sensor(s) 460, coupled to a dedicated processor, DSP, FPGA, and/or ASIC, that is electrically coupled to driver feedback, such as a display, speaker, and/or vibrating component.
Conventional ADAS systems may be prone to false positive results, which may be annoying and distracting to a driver, but typically are not catastrophic, because the ADAS systems alert the driver and allow the driver to decide whether a safety condition truly exists and act accordingly. However, in an autonomous vehicle 400, the vehicle 400 itself must, in the case of conflicting results, decide whether to heed the result from a primary computer or a secondary computer (e.g., a first controller 436 or a second controller 436). For example, in some embodiments, the ADAS system 438 may be a backup and/or secondary computer for providing perception information to a backup computer rationality module. The backup computer rationality monitor may run a redundant diverse software on hardware components to detect faults in perception and dynamic driving tasks. Outputs from the ADAS system 438 may be provided to a supervisory MCU. If outputs from the primary computer and the secondary computer conflict, the supervisory MCU must determine how to reconcile the conflict to ensure safe operation.
In some examples, the primary computer may be configured to provide the supervisory MCU with a confidence score, indicating the primary computer's confidence in the chosen result. If the confidence score exceeds a threshold, the supervisory MCU may follow the primary computer's direction, regardless of whether the secondary computer provides a conflicting or inconsistent result. Where the confidence score does not meet the threshold, and where the primary and secondary computer indicate different results (e.g., the conflict), the supervisory MCU may arbitrate between the computers to determine the appropriate outcome.
The supervisory MCU may be configured to run a neural network(s) that is trained and configured to determine, based on outputs from the primary computer and the secondary computer, conditions under which the secondary computer provides false alarms. Thus, the neural network(s) in the supervisory MCU may learn when the secondary computer's output may be trusted, and when it cannot. For example, when the secondary computer is a RADAR-based FCW system, a neural network(s) in the supervisory MCU may learn when the FCW system is identifying metallic objects that are not, in fact, hazards, such as a drainage grate or manhole cover that triggers an alarm. Similarly, when the secondary computer is a camera-based LDW system, a neural network in the supervisory MCU may learn to override the LDW when bicyclists or pedestrians are present and a lane departure is, in fact, the safest maneuver. In embodiments that include a neural network(s) running on the supervisory MCU, the supervisory MCU may include at least one of a DLA or GPU suitable for running the neural network(s) with associated memory. In preferred embodiments, the supervisory MCU may comprise and/or be included as a component of the SoC(s) 404.
In other examples, ADAS system 438 may include a secondary computer that performs ADAS functionality using traditional rules of computer vision. As such, the secondary computer may use classic computer vision rules (if-then), and the presence of a neural network(s) in the supervisory MCU may improve reliability, safety and performance. For example, the diverse implementation and intentional non-identity makes the overall system more fault-tolerant, especially to faults caused by software (or software-hardware interface) functionality. For example, if there is a software bug or error in the software running on the primary computer, and the non-identical software code running on the secondary computer provides the same overall result, the supervisory MCU may have greater confidence that the overall result is correct, and the bug in software or hardware on primary computer is not causing material error.
In some examples, the output of the ADAS system 438 may be fed into the primary computer's perception block and/or the primary computer's dynamic driving task block. For example, if the ADAS system 438 indicates a forward crash warning due to an object immediately ahead, the perception block may use this information when identifying objects. In other examples, the secondary computer may have its own neural network that is trained and thus reduces the risk of false positives, as described herein.
The vehicle 400 may further include the infotainment SoC 430 (e.g., an in-vehicle infotainment system (IVI)). Although illustrated and described as an SoC, the infotainment system may not be a SoC, and may include two or more discrete components. The infotainment SoC 430 may include a combination of hardware and software that may be used to provide audio (e.g., music, a personal digital assistant, navigational instructions, news, radio, etc.), video (e.g., TV, movies, streaming, etc.), phone (e.g., hands-free calling), network connectivity (e.g., LTE, Wi-Fi, etc.), and/or information services (e.g., navigation systems, rear-parking assistance, a radio data system, vehicle-related information such as fuel level, total distance covered, brake fuel level, oil level, door open/close, air filter information, etc.) to the vehicle 400. For example, the infotainment SoC 430 may include radios, disk players, navigation systems, video players, USB and Bluetooth connectivity, carputers, in-car entertainment, Wi-Fi, steering wheel audio controls, hands-free voice control, a heads-up display (HUD), an HMI display 434, a telematics device, a control panel (e.g., for controlling and/or interacting with various components, features, and/or systems), and/or other components. The infotainment SoC 430 may further be used to provide information (e.g., visual and/or audible) to a user(s) of the vehicle, such as information from the ADAS system 438, autonomous driving information such as planned vehicle maneuvers, trajectories, surrounding environment information (e.g., intersection information, vehicle information, road information, etc.), and/or other information.
The infotainment SoC 430 may include GPU functionality. The infotainment SoC 430 may communicate over the bus 402 (e.g., CAN bus, Ethernet, etc.) with other devices, systems, and/or components of the vehicle 400. In some examples, the infotainment SoC 430 may be coupled to a supervisory MCU such that the GPU of the infotainment system may perform some self-driving functions in the event that the primary controller(s) 436 (e.g., the primary and/or backup computers of the vehicle 400) fail. In such an example, the infotainment SoC 430 may put the vehicle 400 into a chauffeur to safe-stop mode, as described herein.
The vehicle 400 may further include an instrument cluster 432 (e.g., a digital dash, an electronic instrument cluster, a digital instrument panel, etc.). The instrument cluster 432 may include a controller and/or supercomputer (e.g., a discrete controller or supercomputer). The instrument cluster 432 may include a set of instrumentation such as a speedometer, fuel level, oil pressure, tachometer, odometer, turn indicators, gearshift position indicator, seat belt warning light(s), parking-brake warning light(s), engine-malfunction light(s), airbag (SRS) system information, lighting controls, safety system controls, navigation information, etc. In some examples, information may be displayed and/or shared among the infotainment SoC 430 and the instrument cluster 432. In other words, the instrument cluster 432 may be included as part of the infotainment SoC 430, or vice versa.
The server(s) 478 may receive, over the network(s) 490 and from the vehicles, image data representative of images showing unexpected or changed road conditions, such as recently commenced road work. The server(s) 478 may transmit, over the network(s) 490 and to the vehicles, neural networks 492, updated neural networks 492, and/or map information 494, including information regarding traffic and road conditions. The updates to the map information 494 may include updates for the HD map 422, such as information regarding construction sites, potholes, detours, flooding, and/or other obstructions. In some examples, the neural networks 492, the updated neural networks 492, and/or the map information 494 may have resulted from new training and/or experiences represented in data received from any number of vehicles in the environment, and/or based on training performed at a datacenter (e.g., using the server(s) 478 and/or other servers).
The server(s) 478 may be used to train machine learning models (e.g., neural networks) based on training data. The training data may be generated by the vehicles, and/or may be generated in a simulation (e.g., using a game engine). In some examples, the training data is tagged (e.g., where the neural network benefits from supervised learning) and/or undergoes other pre-processing, while in other examples the training data is not tagged and/or pre-processed (e.g., where the neural network does not require supervised learning). Training may be executed according to any one or more classes of machine learning techniques, including, without limitation, classes such as: supervised training, semi-supervised training, unsupervised training, self learning, reinforcement learning, federated learning, transfer learning, feature learning (including principal component and cluster analyses), multi-linear subspace learning, manifold learning, representation learning (including spare dictionary learning), rule-based machine learning, anomaly detection, and any variants or combinations therefor. Once the machine learning models are trained, the machine learning models may be used by the vehicles (e.g., transmitted to the vehicles over the network(s) 490, and/or the machine learning models may be used by the server(s) 478 to remotely monitor the vehicles.
In some examples, the server(s) 478 may receive data from the vehicles and apply the data to up-to-date real-time neural networks for real-time intelligent inferencing. The server(s) 478 may include deep-learning supercomputers and/or dedicated AI computers powered by GPU(s) 484, such as a DGX and DGX Station machines developed by NVIDIA. However, in some examples, the server(s) 478 may include deep learning infrastructure that use only CPU-powered datacenters.
The deep-learning infrastructure of the server(s) 478 may be capable of fast, real-time inferencing, and may use that capability to evaluate and verify the health of the processors, software, and/or associated hardware in the vehicle 400. For example, the deep-learning infrastructure may receive periodic updates from the vehicle 400, such as a sequence of images and/or objects that the vehicle 400 has located in that sequence of images (e.g., via computer vision and/or other machine learning object classification techniques). The deep-learning infrastructure may run its own neural network to identify the objects and compare them with the objects identified by the vehicle 400 and, if the results do not match and the infrastructure concludes that the AI in the vehicle 400 is malfunctioning, the server(s) 478 may transmit a signal to the vehicle 400 instructing a fail-safe computer of the vehicle 400 to assume control, notify the passengers, and complete a safe parking maneuver.
For inferencing, the server(s) 478 may include the GPU(s) 484 and one or more programmable inference accelerators (e.g., NVIDIA's TensorRT). The combination of GPU-powered servers and inference acceleration may make real-time responsiveness possible. In other examples, such as where performance is less critical, servers powered by CPUs, FPGAs, and other processors may be used for inferencing.
Although the various blocks of
The interconnect system 502 may represent one or more links or busses, such as an address bus, a data bus, a control bus, or a combination thereof. The interconnect system 502 may include one or more bus or link types, such as an industry standard architecture (ISA) bus, an extended industry standard architecture (EISA) bus, a video electronics standards association (VESA) bus, a peripheral component interconnect (PCI) bus, a peripheral component interconnect express (PCIe) bus, and/or another type of bus or link. In some embodiments, there are direct connections between components. As an example, the CPU 506 may be directly connected to the memory 504. Further, the CPU 506 may be directly connected to the GPU 508. Where there is direct, or point-to-point, connection between components, the interconnect system 502 may include a PCIe link to carry out the connection. In these examples, a PCI bus need not be included in the computing device 500.
The memory 504 may include any of a variety of computer-readable media. The computer-readable media may be any available media that may be accessed by the computing device 500. The computer-readable media may include both volatile and nonvolatile media, and removable and non-removable media. By way of example, and not limitation, the computer-readable media may comprise computer-storage media and communication media.
The computer-storage media may include both volatile and nonvolatile media and/or removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, and/or other data types. For example, the memory 504 may store computer-readable instructions (e.g., that represent a program(s) and/or a program element(s), such as an operating system. Computer-storage media may include, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which may be used to store the desired information and which may be accessed by computing device 500. As used herein, computer storage media does not comprise signals per se.
The computer storage media may embody computer-readable instructions, data structures, program modules, and/or other data types in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulated data signal” may refer to a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, the computer storage media may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of any of the above should also be included within the scope of computer-readable media.
The CPU(s) 506 may be configured to execute at least some of the computer-readable instructions to control one or more components of the computing device 500 to perform one or more of the methods and/or processes described herein. The CPU(s) 506 may each include one or more cores (e.g., one, two, four, eight, twenty-eight, seventy-two, etc.) that are capable of handling a multitude of software threads simultaneously. The CPU(s) 506 may include any type of processor, and may include different types of processors depending on the type of computing device 500 implemented (e.g., processors with fewer cores for mobile devices and processors with more cores for servers). For example, depending on the type of computing device 500, the processor may be an Advanced RISC Machines (ARM) processor implemented using Reduced Instruction Set Computing (RISC) or an x86 processor implemented using Complex Instruction Set Computing (CISC). The computing device 500 may include one or more CPUs 506 in addition to one or more microprocessors or supplementary co-processors, such as math co-processors.
In addition to or alternatively from the CPU(s) 506, the GPU(s) 508 may be configured to execute at least some of the computer-readable instructions to control one or more components of the computing device 500 to perform one or more of the methods and/or processes described herein. One or more of the GPU(s) 508 may be an integrated GPU (e.g., with one or more of the CPU(s) 506 and/or one or more of the GPU(s) 508 may be a discrete GPU. In embodiments, one or more of the GPU(s) 508 may be a coprocessor of one or more of the CPU(s) 506. The GPU(s) 508 may be used by the computing device 500 to render graphics (e.g., 3D graphics) or perform general purpose computations. For example, the GPU(s) 508 may be used for General-Purpose computing on GPUs (GPGPU). The GPU(s) 508 may include hundreds or thousands of cores that are capable of handling hundreds or thousands of software threads simultaneously. The GPU(s) 508 may generate pixel data for output images in response to rendering commands (e.g., rendering commands from the CPU(s) 506 received via a host interface). The GPU(s) 508 may include graphics memory, such as display memory, for storing pixel data or any other suitable data, such as GPGPU data. The display memory may be included as part of the memory 504. The GPU(s) 508 may include two or more GPUs operating in parallel (e.g., via a link). The link may directly connect the GPUs (e.g., using NVLINK) or may connect the GPUs through a switch (e.g., using NVSwitch). When combined together, each GPU 508 may generate pixel data or GPGPU data for different portions of an output or for different outputs (e.g., a first GPU for a first image and a second GPU for a second image). Each GPU may include its own memory, or may share memory with other GPUs.
In addition to or alternatively from the CPU(s) 506 and/or the GPU(s) 508, the logic unit(s) 520 may be configured to execute at least some of the computer-readable instructions to control one or more components of the computing device 500 to perform one or more of the methods and/or processes described herein. In embodiments, the CPU(s) 506, the GPU(s) 508, and/or the logic unit(s) 520 may discretely or jointly perform any combination of the methods, processes and/or portions thereof. One or more of the logic units 520 may be part of and/or integrated in one or more of the CPU(s) 506 and/or the GPU(s) 508 and/or one or more of the logic units 520 may be discrete components or otherwise external to the CPU(s) 506 and/or the GPU(s) 508. In embodiments, one or more of the logic units 520 may be a coprocessor of one or more of the CPU(s) 506 and/or one or more of the GPU(s) 508.
Examples of the logic unit(s) 520 include one or more processing cores and/or components thereof, such as Data Processing Units (DPUs), Tensor Cores (TCs), Tensor Processing Units (TPUs), Pixel Visual Cores (PVCs), Vision Processing Units (VPUs), Graphics Processing Clusters (GPCs), Texture Processing Clusters (TPCs), Streaming Multiprocessors (SMs), Tree Traversal Units (TTUs), Artificial Intelligence Accelerators (AIAs), Deep Learning
Accelerators (DLAs), Arithmetic-Logic Units (ALUs), Application-Specific Integrated Circuits (ASICs), Floating Point Units (FPUs), input/output (I/O) elements, peripheral component interconnect (PCI) or peripheral component interconnect express (PCIe) elements, and/or the like.
The communication interface 510 may include one or more receivers, transmitters, and/or transceivers that enable the computing device 500 to communicate with other computing devices via an electronic communication network, include wired and/or wireless communications. The communication interface 510 may include components and functionality to enable communication over any of a number of different networks, such as wireless networks (e.g., Wi-Fi, Z-Wave, Bluetooth, Bluetooth LE, ZigBee, etc.), wired networks (e.g., communicating over Ethernet or InfiniBand), low-power wide-area networks (e.g., LoRaWAN, SigFox, etc.), and/or the Internet. In one or more embodiments, logic unit(s) 520 and/or communication interface 510 may include one or more data processing units (DPUs) to transmit data received over a network and/or through interconnect system 502 directly to (e.g., a memory of) one or more GPU(s) 508.
The I/O ports 512 may enable the computing device 500 to be logically coupled to other devices including the I/O components 514, the presentation component(s) 518, and/or other components, some of which may be built in to (e.g., integrated in) the computing device 500. Illustrative I/O components 514 include a microphone, mouse, keyboard, joystick, game pad, game controller, satellite dish, scanner, printer, wireless device, etc. The I/O components 514 may provide a natural user interface (NUI) that processes air gestures, voice, or other physiological inputs generated by a user. In some instances, inputs may be transmitted to an appropriate network element for further processing. An NUI may implement any combination of speech recognition, stylus recognition, facial recognition, biometric recognition, gesture recognition both on screen and adjacent to the screen, air gestures, head and eye tracking, and touch recognition (as described in more detail in the present disclosure) associated with a display of the computing device 500. The computing device 500 may include depth cameras, such as stereoscopic camera systems, infrared camera systems, RGB camera systems, touchscreen technology, and combinations of these, for gesture detection and recognition. Additionally, the computing device 500 may include accelerometers or gyroscopes (e.g., as part of an inertia measurement unit (IMU)) that enable detection of motion. In some examples, the output of the accelerometers or gyroscopes may be used by the computing device 500 to render immersive augmented reality or virtual reality.
The power supply 516 may include a hard-wired power supply, a battery power supply, or a combination thereof. The power supply 516 may provide power to the computing device 500 to enable the components of the computing device 500 to operate.
The presentation component(s) 518 may include a display (e.g., a monitor, a touch screen, a television screen, a heads-up-display (HUD), other display types, or a combination thereof), speakers, and/or other presentation components. The presentation component(s) 518 may receive data from other components (e.g., the GPU(s) 508, the CPU(s) 506, etc.), and output the data (e.g., as an image, video, sound, etc.).
As shown in
In at least one embodiment, grouped computing resources 614 may include separate groupings of node C.R.s 616 housed within one or more racks (not shown), or many racks housed in data centers at various geographical locations (also not shown). Separate groupings of node C.R.s 616 within grouped computing resources 614 may include grouped compute, network, memory or storage resources that may be configured or allocated to support one or more workloads. In at least one embodiment, several node C.R.s 616 including CPUs, GPUs, DPUs, and/or other processors may be grouped within one or more racks to provide compute resources to support one or more workloads. The one or more racks may also include any number of power modules, cooling modules, and/or network switches, in any combination.
The resource orchestrator 612 may configure or otherwise control one or more node C.R.s 616(1)-616(N) and/or grouped computing resources 614. In at least one embodiment, resource orchestrator 612 may include a software design infrastructure (SDI) management entity for the data center 600. The resource orchestrator 612 may include hardware, software, or some combination thereof.
In at least one embodiment, as shown in
In at least one embodiment, software 632 included in software layer 630 may include software used by at least portions of node C.R.s 616(1)-616(N), grouped computing resources 614, and/or distributed file system 638 of framework layer 620. One or more types of software may include, but are not limited to, Internet web page search software, e-mail virus scan software, database software, and streaming video content software.
In at least one embodiment, application(s) 642 included in application layer 640 may include one or more types of applications used by at least portions of node C.R.s 616(1)-616(N), grouped computing resources 614, and/or distributed file system 638 of framework layer 620. One or more types of applications may include, but are not limited to, any number of a genomics application, a cognitive compute, and a machine learning application, including training or inferencing software, machine learning framework software (e.g., PyTorch, TensorFlow, Caffe, etc.), and/or other machine learning applications used in conjunction with one or more embodiments.
In at least one embodiment, any of configuration manager 634, resource manager 636, and resource orchestrator 612 may implement any number and type of self-modifying actions based on any amount and type of data acquired in any technically feasible fashion. Self-modifying actions may relieve a data center operator of data center 600 from making possibly bad configuration decisions and possibly avoiding underutilized and/or poor performing portions of a data center.
The data center 600 may include tools, services, software or other resources to train one or more machine learning models or predict or infer information using one or more machine learning models according to one or more embodiments described herein. For example, a machine learning model(s) may be trained by calculating weight parameters according to a neural network architecture using software and/or computing resources described in the present disclosure with respect to the data center 600. In at least one embodiment, trained or deployed machine learning models corresponding to one or more neural networks may be used to infer or predict information using resources described in the present disclosure with respect to the data center 600 by using weight parameters calculated through one or more training techniques, such as but not limited to those described herein.
In at least one embodiment, the data center 600 may use CPUs, application-specific integrated circuits (ASICs), GPUs, FPGAs, and/or other hardware (or virtual compute resources corresponding thereto) to perform training and/or inferencing using above-described resources. Moreover, one or more software and/or hardware resources described in the present disclosure may be configured as a service to allow users to train or performing inferencing of information, such as image recognition, speech recognition, or other artificial intelligence services.
Network environments suitable for use in implementing embodiments of the disclosure may include one or more client devices, servers, network attached storage (NAS), other backend devices, and/or other device types. The client devices, servers, and/or other device types (e.g., each device) may be implemented on one or more instances of the computing device(s) 500 of
Components of a network environment may communicate with each other via a network(s), which may be wired, wireless, or both. The network may include multiple networks, or a network of networks. By way of example, the network may include one or more Wide Area Networks (WANs), one or more Local Area Networks (LANs), one or more public networks such as the Internet and/or a public switched telephone network (PSTN), and/or one or more private networks. Where the network includes a wireless telecommunications network, components such as a base station, a communications tower, or even access points (as well as other components) may provide wireless connectivity.
Compatible network environments may include one or more peer-to-peer network environments—in which case a server may not be included in a network environment—and one or more client-server network environments—in which case one or more servers may be included in a network environment. In peer-to-peer network environments, functionality described herein with respect to a server(s) may be implemented on any number of client devices.
In at least one embodiment, a network environment may include one or more cloud-based network environments, a distributed computing environment, a combination thereof, etc. A cloud-based network environment may include a framework layer, a job scheduler, a resource manager, and a distributed file system implemented on one or more of servers, which may include one or more core network servers and/or edge servers. A framework layer may include a framework to support software of a software layer and/or one or more application(s) of an application layer. The software or application(s) may respectively include web-based service software or applications. In embodiments, one or more of the client devices may use the web-based service software or applications (e.g., by accessing the service software and/or applications via one or more application programming interfaces (APIs)). The framework layer may be, but is not limited to, a type of free and open-source software web application framework such as that may use a distributed file system for large-scale data processing (e.g., “big data”).
A cloud-based network environment may provide cloud computing and/or cloud storage that carries out any combination of computing and/or data storage functions described herein (or one or more portions thereof). Any of these various functions may be distributed over multiple locations from central or core servers (e.g., of one or more data centers that may be distributed across a state, a region, a country, the globe, etc.). If a connection to a user (e.g., a client device) is relatively close to an edge server(s), a core server(s) may designate at least a portion of the functionality to the edge server(s). A cloud-based network environment may be private (e.g., limited to a single organization), may be public (e.g., available to many organizations), and/or a combination thereof (e.g., a hybrid cloud environment).
The client device(s) may include at least some of the components, features, and functionality of the example computing device(s) 500 described herein with respect to
The disclosure may be described in the general context of computer code or machine-useable instructions, including computer-executable instructions such as program modules, being executed by a computer or other machine, such as a personal data assistant or other handheld device. Generally, program modules including routines, programs, objects, components, data structures, etc., refer to codes that perform particular tasks or implement particular abstract data types. The disclosure may be practiced in a variety of system configurations, including hand-held devices, consumer electronics, general-purpose computers, more specialty computing devices, etc. The disclosure may also be practiced in distributed computing environments where tasks are performed by remote-processing devices that are linked through a communications network.
As used herein, a recitation of “and/or” with respect to two or more elements should be interpreted to mean only one element, or a combination of elements. For example, “element A, element B, and/or element C” may include only element A, only element B, only element C, element A and element B, element A and element C, element B and element C, or elements A, B, and C. In addition, “at least one of element A or element B” may include at least one of element A, at least one of element B, or at least one of element A and at least one of element B. Further, “at least one of element A and element B” may include at least one of element A, at least one of element B, or at least one of element A and at least one of element B. Additionally, use of the term “based on” should not be interpreted as “only based on” or “based only on.” Rather, a first element being “based on” a second element includes instances in which the first element is based on the second element but may also be based on one or more additional elements.
The subject matter of the present disclosure is described with specificity herein to meet statutory requirements. However, the description itself is not intended to limit the scope of this disclosure. Rather, the inventors have contemplated that the claimed subject matter might also be embodied in other ways, to include different steps or combinations of steps similar to the ones described in this document, in conjunction with other present or future technologies. Moreover, although the terms “step” and/or “block” may be used herein to connote different elements of methods employed, the terms should not be interpreted as implying any particular order among or between various steps herein disclosed unless and except when the order of individual steps is explicitly described.
