Patent Application
20230126823
The present disclosure involves artificial intelligence systems and methods.
Systems such as a home network may contain dedicated resources to manage services in the home in connection with/at the request of heterogeneous consumer electronics (CE) devices in the home. For example, such services can include artificial intelligence (AI) resources, systems and methods used to control CE devices, e.g., by learning and adapting to any of a plurality of variables such as the environment in which devices are located, user(s) of the device, etc. An aspect of such services can be a system or device referred to herein as an “AI hub”, a boosted AI CPE (“consumer premises equipment” such as STB, gateway, edge computing resources, etc.). This can be a central node within the system to provide, for example, a) virtualization environment to host AI micro services and b) ensure interoperability with connected CE devices or Edge computing, access to services and resources (compute, storage, video processing, AI/ML (machine learning) accelerator). In addition, an AI hub can offload computational AI tasks to other CE devices registered in the Home Data Center.
In general, at least one example of an embodiment described herein involves an AI system and method that can adapt its configuration or architecture based on an instruction.
In general, at least one other example of an embodiment involves a neural network system and method that can communicate with and/or be driven by a control device to adapt a configuration of the neural network based on a constraint.
In general, at least one example of an embodiment involves apparatus comprising: one or more processors configured to determine a constraint associated with processing a sequence of data; adapt a neural network based on the constraint, wherein the one or more processors being configured to adapt the neural network comprises the one or more processors being configured to modify, based on the constraint, a characteristic of a decision function included in the neural network; and enable processing of at least a first portion of the sequence of data utilizing the adapted neural network and in accordance with the constraint.
In general, at least one example of an embodiment involves a method comprising: determining a constraint associated with processing a sequence of data; adapting a neural network based on the constraint, wherein adapting the neural network comprises modifying, based on the constraint, a characteristic of a decision function included in the neural network; and enabling processing at least a first portion of the sequence of data utilizing the adapted neural network in accordance with the constraint.
In general, at least one example of an embodiment involves apparatus comprising: one or more processors configured to implement a neural network including a decision function; adapt the neural network based on a constraint, wherein the one or more processors being configured to adapt the neural network comprises the one or more processors being configured to modify a characteristic of the decision function based on the constraint; and process data utilizing the adapted neural network in accordance with the constraint.
In general, at least one example of an embodiment involves a method comprising: implementing a neural network including a decision function; adapting the neural network based on a constraint, wherein adapting the neural network comprises modifying a characteristic of the decision function associated with the neural network based on the constraint; and processing data utilizing the adapted neural network in accordance with the constraint.
In general, at least one example of an embodiment involves apparatus comprising: one or more processors configured to implement a neural network including a decision function; adapt the neural network based on a constraint, wherein the one or more processors being configured to adapt the neural network comprises the one or more processors being configured to modify one or more parameters of the decision function based on the constraint; and process data utilizing the adapted neural network in accordance with the constraint.
In general, at least one example of an embodiment involves a method comprising: implementing a neural network including a decision function; adapting the neural network based on a constraint, wherein adapting the neural network comprises modifying one or more parameters of the decision function associated with the neural network based on the constraint; and processing data utilizing the adapted neural network in accordance with the constraint.
In general, at least one example of an embodiment involves apparatus comprising: one or more processors configured to adapt a neural network including a decision function based on a constraint, wherein the one or more processors being configured to adapt the neural network comprises the one or more processors being configured to modify one or more parameters of the decision function based on the constraint; and process data utilizing the adapted neural network in accordance with the constraint.
In general, at least one example of an embodiment involves a method comprising: adapting a neural network based on a constraint, wherein adapting the neural network comprises modifying, based on the constraint, one or more parameters of a decision function associated with the neural network; and processing data utilizing the adapted neural network in accordance with the constraint.
In general, at least one other example of an embodiment involves a neural network (RNN) system and method that can communicate with and/or be driven by a control device to adapt a configuration of the neural network based on a constraint, where the constraint comprises at least one of a resource availability or an accuracy requirement.
In general, at least one other example of an embodiment provides a recurrent neural network (RNN) system and method that can communicate with and/or be driven by a control device to adapt a configuration of the RNN based on a resource availability.
In general, at least one other example of an embodiment provides a recurrent neural network (RNN) system and method that can communicate with and/or be driven by an orchestrator/scheduler to modify a computational cost of the RNN based on modifying a characteristic of the RNN.
In general, at least one other example of an embodiment provides a recurrent neural network (RNN) system and method that can communicate with and/or be driven by an orchestrator/scheduler to modify a computational cost of the RNN based on modifying a characteristic of the RNN, wherein the characteristic comprises a decision function of the RNN.
In general, at least one other example of an embodiment provides a recurrent neural network (RNN) system and method that can communicate with and/or be driven by an orchestrator/scheduler to modify a computational cost of the RNN based on modifying a characteristic of the RNN, wherein the characteristic comprises a decision function of at least one cell of the RNN.
In general, at least one other example of an embodiment provides a recurrent neural network (RNN) system and method that can communicate with and/or be driven by an orchestrator/scheduler to modify a computational cost of the RNN based on modifying a characteristic of the RNN, wherein the characteristic comprises a decision function of at least one cell of the RNN and the decision function comprises determining whether to update the at least one cell of the RNN.
In general, at least one other example of an embodiment provides a recurrent neural network (RNN) system and method that can communicate with and/or be driven by an orchestrator/scheduler to modify a computational cost of the RNN based on modifying a characteristic of the RNN, wherein the characteristic comprises a decision function of at least one cell of the RNN and the decision function comprises determining how many hidden states of the RNN to update.
In general, at least one other example of an embodiment provides a recurrent neural network (RNN) system and method that can communicate with and/or be driven by an orchestrator/scheduler to modify a computational cost of the RNN based on modifying a characteristic of the RNN, wherein the characteristic comprises a decision function of at least one cell of the RNN, and the decision function comprises determining whether to update the at least one cell of the RNN, and modifying the characteristic comprises modifying at least one parameter of the decision function.
In general, at least one other example of an embodiment provides a recurrent neural network (RNN) system and method that can communicate with and/or be driven by an orchestrator/scheduler to modify a computational cost of the RNN based on modifying a characteristic of the RNN, wherein the characteristic comprises a decision function of at least one cell of the RNN, and the decision function comprises determining whether to update the at least one cell of the RNN, modifying the characteristic comprises modifying at least one parameter of the decision function, and the at least one parameter comprises a binarization function associated with one or more perceptrons of the RNN.
In general, at least one other example of an embodiment provides a recurrent neural network (RNN) system and method that can communicate with and/or be driven by an orchestrator/scheduler to modify a computational cost of the RNN based on adjusting a value of at least one parameter of the RNN, wherein the at least one parameter comprises fbinarize associated with one or more perceptrons of the RNN.
In general, at least one other example of an embodiment provides a method for controlling a computational cost of RNN by an orchestrator/scheduler.
The above presents a simplified summary of the subject matter in order to provide a basic understanding of some aspects of the present disclosure. This summary is not an extensive overview of the subject matter. It is not intended to identify key/critical elements of the embodiments or to delineate the scope of the subject matter. Its sole purpose is to present some concepts of the subject matter in a simplified form as a prelude to the more detailed description provided below.
The present disclosure may be better understood by considering the detailed description below in conjunction with the accompanying figures, in which:
It should be understood that the drawings are for purposes of illustrating examples of various aspects, features and embodiments in accordance with the present disclosure and are not necessarily the only possible configurations. Throughout the various figures, like reference designators refer to the same or similar features.
One aspect of AI hub functionality involves allocating computational resources to various AI services. At some point, the demand may exceed the available resources and a control system, or processor, or software, generally referred to herein as an “orchestrator”, will operate to limit resources available to some or all services. An orchestrator/scheduler can provide for controlling where and when learning models are executed. For example, an orchestrator/scheduler may provide at least one or more of the following functionalities:
An aspect of the present disclosure involves providing systems and methods that avoid severe disruption or shutdown by enabling adaptation to constraints such as resource requirements and/or resource availability (e.g., computational resource availability or requirements) and/or accuracy requirements. In general, at least one example of an embodiment described herein involves a flexible AI system that can receive an instruction or instructions from an orchestrator or a scheduler running the AI hub and adapt its configuration or architecture or model in accordance with the instruction. For example, an instruction might be based on, or provide an indication of, constraints such as current resource requirements or availability or accuracy and instruct the neural network to change one or more characteristics or parameters to adapt to the current constraints. If the constraint or constraints change then one or more additional instructions can be provided to further adapt the neural network to the changed constraint.
The use of an orchestrator and flexible AI systems to maintain a reasonable quality of service may also be implemented on a single device running multiple AI processes. For example, a device such as a smartphone can contain dedicated hardware to accelerate AI processes and enabling such devices to run or provide the functionality of an orchestrator. Other possible devices include smart cars, computers, home assistants or other devices capable of communication via a network such as a home network, e.g., Internet of things, or IoT devices.
In addition, edge computing may involve AI processes and associated resource constraints, e.g., where cloud services are run on edge computing nodes close to the user. As an example, when processes are moved to a new edge node, constraints such as resource availability, e.g., computational resource availability, might be different.
A deep neural network (DNN) is a complex function. A DNN is composed of several neural layers (typically in series) and each neural layer is composed of several perceptrons. A perceptron is a function involving a linear combination of the inputs and a non-linear function, for example a sigmoid function. Trained by a machine learning algorithm on huge data sets, these models have recently proven extremely useful for a wide range of applications and have led to significant improvements to the state-of-the-art in artificial intelligence, computer vision, audio processing and several other domains.
Recurrent neural networks (RNN) denote a class of deep learning architectures specifically designed to process sequences such as sound, videos, text or sensor data. RNN are widely used for such data. Frequently used RNN architectures include long short-term memory (LSTM) networks and gated recurrent units (GRU). Typically, RNN maintain a “state”, a vector of variables, over time. This state is supposed to accumulate relevant information and is updated recursively. At a high-level, this is like hidden Markov models. Each input of the sequence is typically a) processed by some deep layers and b) then combined with the previous state through some other deep layers to compute the new state. Hence, the RNN can be seen as a function taking a sequence of inputs x=(x1, . . . , xT) and recursively computing a set of states s=(s1, . . . , sT). Each state st is computed from st−1 and xt by a cell S of the RNN.
Fully processing the input can be resource intensive. In a constrained environment, this may be undesirable. An approach to reduce the computational load of RNNs involves a “skip-RNN” architecture. This architecture is designed to allow the model to skip some inputs by introducing a state update gate. This part of the model is trained along with the other parameters of the model to maximize accuracy while limiting computational cost. The resulting architecture can be described as follows:
u
t
=f
binarize(ũt)
s
t
=u
t
S(st−1,xt)+(1−ut)st−1
Δút=σ(Wst+b)
ũ
t+1
=u
t
Δũ
t+(1−ut)(ũt+min(Δũt,1−ũt)).
In these equations, fbinarize denotes a binarization function (in other words, the output is 0 if the input is smaller than 0.5 and 1 otherwise), σ a non-linear function and W and b the trainable parameters of the linear part of the state update gate (a perceptron). fbinarize can also be a stochastic sampling from a Bernoulli distribution whose parameter is the input ũt.
This model is trained on a dataset containing a set of input sequences and label(s) associated to each sequence. The model is trained to minimize a loss computed on this labeled data. The loss is the sum of two terms: one term related to the accuracy of the task (for example cross-entropy for classification or Euclidian loss for regression), and a second term that penalizes computational operations: Lbudget=λΣtut, where λ is a weight controlling the strength of the penalty and ut, as defined above, is 0 if there is no update to the state and 1 if there is.
There are other approaches similar to skipRNN that propose an alternative mechanism to reduce computation dynamically based on inputs, for example by also skipping some or only updating part of the state vector. These mechanisms include a decision function, similar to the equations described above. Examples of other approaches include Jump-LSTM, Skim-RNN, VCRNN, and G-LSTM.
Skip-RNN and other related approaches aim to reduce computation while maintaining accuracy. While they allow the system to run using fewer computational resources, the system is fixed and cannot adapt to changing computational constraints. Furthermore, these approaches do not provide for communication with an orchestrator/scheduler.
In general, at least one example of an embodiment described herein involves an artificial intelligence system or method, e.g., a system or method based on a Recurrent Neural Network (RNN) architecture, that can be controlled, e.g., by an orchestrator, to adapt its computation to available computational resources. In fact, there is no RNN architecture that can adapt to changing computational resources. Therefore, RNNs running on shared hardware might be shut down if other processes require the use of the resources. This includes both multiple networks running on the same hardware (for example, a smartphone or a car) or on different devices (such as a home system with heterogeneous devices).
In general, at least one example of an embodiment described herein involves an AI system and method that can adapt its configuration or architecture based on an instruction.
In general, at least one other example of an embodiment provides a recurrent neural network (RNN) system and method that can communicate with and/or be driven by a control device to adapt a configuration of the RNN based on a resource availability.
In general, at least one other example of an embodiment provides a recurrent neural network (RNN) system and method that can communicate with and/or be driven by an orchestrator/scheduler to modify a computational cost of the RNN based on modifying a characteristic of the RNN.
In general, at least one other example of an embodiment provides a recurrent neural network (RNN) system and method that can communicate with and/or be driven by an orchestrator/scheduler to modify a computational cost of the RNN based on modifying a characteristic of the RNN, wherein the characteristic comprises a decision function of the RNN.
In general, at least one other example of an embodiment provides a recurrent neural network (RNN) system and method that can communicate with and/or be driven by an orchestrator/scheduler to modify a computational cost of the RNN based on modifying a characteristic of the RNN, wherein the characteristic comprises a decision function of at least one cell of the RNN.
In general, at least one other example of an embodiment provides a recurrent neural network (RNN) system and method that can communicate with and/or be driven by an orchestrator/scheduler to modify a computational cost of the RNN based on modifying a characteristic of the RNN, wherein the characteristic comprises a decision function of at least one cell of the RNN and the decision function comprises determining whether to update the at least one cell of the RNN.
In general, at least one other example of an embodiment provides a recurrent neural network (RNN) system and method that can communicate with and/or be driven by an orchestrator/scheduler to modify a computational cost of the RNN based on modifying a characteristic of the RNN, wherein the characteristic comprises a decision function of at least one cell of the RNN and the decision function comprises determining how many hidden states of the RNN to update
In general, at least one other example of an embodiment provides a recurrent neural network (RNN) system and method that can communicate with and/or be driven by an orchestrator/scheduler to modify a computational cost of the RNN based on modifying a characteristic of the RNN, wherein the characteristic comprises a decision function of at least one cell of the RNN, and the decision function comprises determining whether to update the at least one cell of the RNN, and modifying the characteristic comprises modifying at least one parameter of the decision function.
In general, at least one other example of an embodiment provides a recurrent neural network (RNN) system and method that can communicate with and/or be driven by an orchestrator/scheduler to modify a computational cost of the RNN based on modifying a characteristic of the RNN, wherein the characteristic comprises a decision function of at least one cell of the RNN, and the decision function comprises determining whether to update the at least one cell of the RNN, modifying the characteristic comprises modifying at least one parameter of the decision function, and the at least one parameter comprises a binarization function associated with one or more perceptrons of the RNN.
In general, at least one other example of an embodiment provides a method for controlling a computational cost of RNN by an orchestrator/scheduler.
The following first describes the architecture that allows the model to adapt its computational cost and then describes how to allow an orchestrator/scheduler to leverage this capacity.
In general, at least one example of an embodiment involves varying a computational cost of an AI system or method based on an architecture such as RNN. In an embodiment, an architecture of the model can be similar to the skipRNN model. However, the decision function is different in that the decision function can be modified based on constraints such as computational resources, computational cost, and/or accuracy requirements. The modification of the decision function can occur based on an indication of such constraints where, for example, the indication is provided by a control feature such as an orchestrator or scheduler. As an example of modification of the decision function, the decision function may include one or more characteristics or parameters that can be modified. In accordance with one aspect of the present disclosure, one such characteristic or parameter, fbinarize is different than described above. For example, the function accepts an additional parameter, thr, that may be viewed as a threshold value:
Varying thr changes the behavior of the model. When it increases, the model will skip more inputs. At the same time, the model still adapts its computation to the data. Modifying thr gracefully trades off accuracy for computational cost as explained in more detail below.
This architecture can be trained like the skipRNN model. Implementing the gates as described above allows the model to be trained on minibatches of data using one or more processors such as graphics processing units (GPUs) and stochastic gradient descent. The model can be trained with a fixed thr and used as it. It could also be trained by varying that parameter during training, either to fixed but different values for each minibatch or to different values for different points in the sequence.
At inference, thr can be modified dynamically. An example of an approach to implement this is to modify the input of the model. In addition to the inputs x=(x1, . . . , xT), the model can receive a sequence of parameters thr=(thr1, . . . , thrT). The parameters can be generated by the orchestrator/scheduler, or the system itself. How these parameters can be chosen to achieve a desired result is described below in detail with regard to both a first example involving parameter generation by an orchestrator/scheduler and a second example involving generation by the system itself. For example, a sequence of parameters could be (0.5, 0.5, 0.5, . . . , 0.5, 0.6, 0.6, . . . 0.6). When the value changes from 0.5 to 0.6, the model will use fewer computational resources. These parameters can then be fed to the fbinarize.
As an alternative, the parameter can be static, that is, stored in the model, and changed when necessary. For the static use case, the parameter could be changed through different methods, e.g.:
As in skipRNN, inference would typically be performed with a different implementation than for training. Regarding inference/training differences, both architectures can be used for any task. However, using the multiplicative implementation (described in the skip-RNN equations) is likely to be much faster for training. Using the conditional implementation (e.g., tf.cond function of the tensorflow deep learning framework) is better for inference. Using the multiplicative implementation for inference will not save any computation and thus will be pointless. When using a deep learning framework such as tensorflow or Pytorch, and depending on the framework used, the training implementation will typically not achieve any computational gain as both the skip and the non-skip operation are computed at every time step. For inference, the condition ũt<thr must be evaluated before computing unnecessary values and, if true, the input must be skipped and related computations must be performed. This can for example be achieved using eager execution or by using conditional operators such as tf.cond.
An example of an embodiment is now described with respect to
Results, presented in
Modifying thr “smoothly” as mentioned above means the following.
The architecture described above has a computational cost that can be tuned by varying thr. Examples of embodiments for controlling thr include, but are not limited to, the following.
For any one or more of the described examples of embodiments, the requests of the orchestrator/scheduler (for example thr values) may be provided as input to the model (for example as an additional element in the vector x=(x1, . . . , xT)). The orchestrator/scheduler may also communicate with the model through other appropriate mechanisms such as:
This document describes various examples of embodiments, features, models, approaches, etc. Many such examples are described with specificity and, at least to show the individual characteristics, are often described in a manner that may appear limiting. However, this is for purposes of clarity in description, and does not limit the application or scope. Indeed, the various examples of embodiments, features, etc., described herein can be combined and interchanged in various ways to provide further examples of embodiments.
In general, the examples of embodiments described and contemplated in this document can be implemented in many different forms. For example,
Various methods are described herein, and each of the methods comprises one or more steps or actions for achieving the described method. Unless a specific order of steps or actions is required for proper operation of the method, the order and/or use of specific steps and/or actions may be modified or combined.
Various embodiments, e.g., methods, and other aspects described in this document can be used to modify a system such as the example shown in
Various numeric values are used in the present document, for example. The specific values are for example purposes and the aspects described are not limited to these specific values.
The system 1000 includes at least one processor 1010 configured to execute instructions loaded therein for implementing, for example, the various aspects described in this document. Processor 1010 can include embedded memory, input output interface, and various other circuitries as known in the art. The system 1000 includes at least one memory 1020 (e.g., a volatile memory device, and/or a non-volatile memory device). System 1000 includes a storage device 1040, which can include non-volatile memory and/or volatile memory, including, but not limited to, EEPROM, ROM, PROM, RAM, DRAM, SRAM, flash, magnetic disk drive, and/or optical disk drive. The storage device 1040 can include an internal storage device, an attached storage device, and/or a network accessible storage device, as non-limiting examples.
System 1000 can include an encoder/decoder module 1030 configured, for example, to process image data to provide an encoded video or decoded video, and the encoder/decoder module 1030 can include its own processor and memory. The encoder/decoder module 1030 represents module(s) that can be included in a device to perform the encoding and/or decoding functions. As is known, a device can include one or both of the encoding and decoding modules. Additionally, encoder/decoder module 1030 can be implemented as a separate element of system 1000 or can be incorporated within processor 1010 as a combination of hardware and software as known to those skilled in the art.
Program code to be loaded onto processor 1010 or encoder/decoder 1030 to perform the various aspects described in this document can be stored in storage device 1040 and subsequently loaded onto memory 1020 for execution by processor 1010. In accordance with various embodiments, one or more of processor 1010, memory 1020, storage device 1040, and encoder/decoder module 1030 can store one or more of various items during the performance of the processes described in this document. Such stored items can include, but are not limited to, the input video, the decoded video or portions of the decoded video, the bitstream or signal, matrices, variables, and intermediate or final results from the processing of equations, formulas, operations, and operational logic.
In several embodiments, memory inside of the processor 1010 and/or the encoder/decoder module 1030 is used to store instructions and to provide working memory for processing that is needed during operations such as those described herein. In other embodiments, however, a memory external to the processing device (for example, the processing device can be either the processor 1010 or the encoder/decoder module 1030) is used for one or more of these functions. The external memory can be the memory 1020 and/or the storage device 1040, for example, a dynamic volatile memory and/or a non-volatile flash memory. In several embodiments, an external non-volatile flash memory is used to store the operating system of a television. In at least one embodiment, a fast external dynamic volatile memory such as a RAM is used as working memory for video coding and decoding operations, such as for MPEG-2, HEVC, or VVC (Versatile Video Coding).
The input to the elements of system 1000 can be provided through various input devices as indicated in block 1130. Such input devices include, but are not limited to, (i) an RF portion that receives an RF signal transmitted, for example, over the air by a broadcaster, (ii) a Composite input terminal, (iii) a USB input terminal, and/or (iv) an HDMI input terminal.
In various embodiments, the input devices of block 1130 have associated respective input processing elements as known in the art. For example, the RF portion can be associated with elements for (i) selecting a desired frequency (also referred to as selecting a signal, or band-limiting a signal to a band of frequencies), (ii) downconverting the selected signal, (iii) band-limiting again to a narrower band of frequencies to select (for example) a signal frequency band which can be referred to as a channel in certain embodiments, (iv) demodulating the downconverted and band-limited signal, (v) performing error correction, and (vi) demultiplexing to select the desired stream of data packets. The RF portion of various embodiments includes one or more elements to perform these functions, for example, frequency selectors, signal selectors, band-limiters, channel selectors, filters, downconverters, demodulators, error correctors, and demultiplexers. The RF portion can include a tuner that performs various of these functions, including, for example, downconverting the received signal to a lower frequency (for example, an intermediate frequency or a near-baseband frequency) or to baseband. In one set-top box embodiment, the RF portion and its associated input processing element receives an RF signal transmitted over a wired (for example, cable) medium, and performs frequency selection by filtering, downconverting, and filtering again to a desired frequency band. Various embodiments rearrange the order of the above-described (and other) elements, remove some of these elements, and/or add other elements performing similar or different functions. Adding elements can include inserting elements in between existing elements, for example, inserting amplifiers and an analog-to-digital converter. In various embodiments, the RF portion includes an antenna.
Additionally, the USB and/or HDMI terminals can include respective interface processors for connecting system 1000 to other electronic devices across USB and/or HDMI connections. It is to be understood that various aspects of input processing, for example, Reed-Solomon error correction, can be implemented, for example, within a separate input processing IC or within processor 1010. Similarly, aspects of USB or HDMI interface processing can be implemented within separate interface ICs or within processor 1010. The demodulated, error corrected, and demultiplexed stream is provided to various processing elements, including, for example, processor 1010, and encoder/decoder 1030 operating in combination with the memory and storage elements to process the datastream for presentation on an output device.
Various elements of system 1000 can be provided within an integrated housing, Within the integrated housing, the various elements can be interconnected and transmit data therebetween using suitable connection arrangement 1140, for example, an internal bus as known in the art, including the I2C bus, wiring, and printed circuit boards.
The system 1000 includes communication interface 1050 that enables communication with other devices via communication channel 1060. The communication interface 1050 can include, but is not limited to, a transceiver configured to transmit and to receive data over communication channel 1060. The communication interface 1050 can include, but is not limited to, a modem or network card and the communication channel 1060 can be implemented, for example, within a wired and/or a wireless medium.
Data is streamed to the system 1000, in various embodiments, using a Wi-Fi network such as IEEE 802.11. The Wi-Fi signal of these embodiments is received over the communications channel 1060 and the communications interface 1050 which are adapted for Wi-Fi communications. The communications channel 1060 of these embodiments is typically connected to an access point or router that provides access to outside networks including the Internet for allowing streaming applications and other over-the-top communications. Other embodiments provide streamed data to the system 1000 using a set-top box that delivers the data over the HDMI connection of the input block 1130. Still other embodiments provide streamed data to the system 1000 using the RF connection of the input block 1130.
The system 1000 can provide an output signal to various output devices, including a display 1100, speakers 1110, and other peripheral devices 1120. The other peripheral devices 1120 include, in various examples of embodiments, one or more of a stand-alone DVR, a disk player, a stereo system, a lighting system, and other devices that provide a function based on the output of the system 1000. In various embodiments, control signals are communicated between the system 1000 and the display 1100, speakers 1110, or other peripheral devices 1120 using signaling such as AV.Link, CEC, or other communications protocols that enable device-to-device control with or without user intervention. The output devices can be communicatively coupled to system 1000 via dedicated connections through respective interfaces 1070, 1080, and 1090. Alternatively, the output devices can be connected to system 1000 using the communications channel 1060 via the communications interface 1050. The display 1100 and speakers 1110 can be integrated in a single unit with the other components of system 1000 in an electronic device, for example, a television. In various embodiments, the display interface 1070 includes a display driver, for example, a timing controller (T Con) chip.
The display 1100 and speaker 1110 can alternatively be separate from one or more of the other components, for example, if the RF portion of input 1130 is part of a separate set-top box. In various embodiments in which the display 1100 and speakers 1110 are external components, the output signal can be provided via dedicated output connections, including, for example, HDMI ports, USB ports, or COMP outputs.
The embodiments can be carried out by computer software implemented by the processor 1010 or by hardware, or by a combination of hardware and software. As a non-limiting example, the embodiments can be implemented by one or more integrated circuits. The memory 1020 can be of any type appropriate to the technical environment and can be implemented using any appropriate data storage technology, such as optical memory devices, magnetic memory devices, semiconductor-based memory devices, fixed memory, and removable memory, as non-limiting examples. The processor 1010 can be of any type appropriate to the technical environment, and can encompass one or more of microprocessors, general purpose computers, special purpose computers, and processors based on a multi-core architecture, as non-limiting examples.
Various generalized as well as particularized embodiments are also supported and contemplated throughout this disclosure. Examples of embodiments in accordance with the present disclosure include but are not limited to the following.
In general, at least one example of an embodiment provides apparatus comprising: one or more processors configured to determine a constraint associated with processing a sequence of data; adapt a neural network based on the constraint, wherein the one or more processors being configured to adapt the neural network comprises the one or more processors being configured to modify, based on the constraint, a characteristic of a decision function included in the neural network; and enable processing of at least a first portion of the sequence of data utilizing the adapted neural network and in accordance with the constraint.
In general, at least one example of an embodiment provides a method comprising: determining a constraint associated with processing a sequence of data; adapting a neural network based on the constraint, wherein adapting the neural network comprises modifying, based on the constraint, a characteristic of a decision function included in the neural network; and enabling processing at least a first portion of the sequence of data utilizing the adapted neural network in accordance with the constraint.
In general, at least one example of an embodiment provides apparatus comprising: one or more processors configured to implement a neural network including a decision function; adapt the neural network based on a constraint, wherein the one or more processors being configured to adapt the neural network comprises the one or more processors being configured to modify a characteristic of the decision function based on the constraint; and process data utilizing the adapted neural network in accordance with the constraint.
In general, at least one example of an embodiment provides a method comprising: implementing a neural network including a decision function; adapting the neural network based on a constraint, wherein adapting the neural network comprises modifying a characteristic of the decision function associated with the neural network based on the constraint; and processing data utilizing the adapted neural network in accordance with the constraint.
In general, at least one example of an embodiment provides apparatus comprising: one or more processors configured to implement a neural network including a decision function; adapt the neural network based on a constraint, wherein the one or more processors being configured to adapt the neural network comprises the one or more processors being configured to modify one or more parameters of the decision function based on the constraint; and process data utilizing the adapted neural network in accordance with the constraint.
In general, at least one example of an embodiment provides a method comprising: implementing a neural network including a decision function; adapting the neural network based on a constraint, wherein adapting the neural network comprises modifying one or more parameters of the decision function associated with the neural network based on the constraint; and processing data utilizing the adapted neural network in accordance with the constraint.
In general, at least one example of an embodiment provides apparatus comprising: one or more processors configured to adapt a neural network including a decision function based on a constraint, wherein the one or more processors being configured to adapt the neural network comprises the one or more processors being configured to modify one or more parameters of the decision function based on the constraint; and process data utilizing the adapted neural network in accordance with the constraint.
In general, at least one example of an embodiment provides a method comprising: adapting a neural network based on a constraint, wherein adapting the neural network comprises modifying, based on the constraint, one or more parameters of a decision function associated with the neural network; and processing data utilizing the adapted neural network in accordance with the constraint.
In general, at least one example of an embodiment provides a recurrent neural network (RNN) system and method that can communicate with and/or be driven by an orchestrator/scheduler to modify a computational cost of the RNN based on modifying a characteristic of the RNN.
In general, at least one other example of an embodiment provides a recurrent neural network (RNN) system and method that can communicate with and/or be driven by an orchestrator/scheduler to modify a computational cost of the RNN based on modifying a characteristic of the RNN, wherein the characteristic comprises a decision function of the RNN.
In general, at least one other example of an embodiment provides a recurrent neural network (RNN) system and method that can communicate with and/or be driven by an orchestrator/scheduler to modify a computational cost of the RNN based on modifying a characteristic of the RNN, wherein the characteristic comprises a decision function of at least one cell of the RNN.
In general, at least one other example of an embodiment provides a recurrent neural network (RNN) system and method that can communicate with and/or be driven by an orchestrator/scheduler to modify a computational cost of the RNN based on modifying a characteristic of the RNN, wherein the characteristic comprises a decision function of at least one cell of the RNN and the decision function comprises determining whether to update the at least one cell of the RNN.
In general, at least one other example of an embodiment provides a recurrent neural network (RNN) system and method that can communicate with and/or be driven by an orchestrator/scheduler to modify a computational cost of the RNN based on modifying a characteristic of the RNN, wherein the characteristic comprises a decision function of at least one cell of the RNN and the decision function comprises determining how many hidden states of the RNN to update.
In general, at least one other example of an embodiment provides a recurrent neural network (RNN) system and method that can communicate with and/or be driven by an orchestrator/scheduler to modify a computational cost of the RNN based on modifying a characteristic of the RNN, wherein the characteristic comprises a decision function of at least one cell of the RNN, and the decision function comprises determining whether to update the at least one cell of the RNN, and modifying the characteristic comprises modifying at least one parameter of the decision function.
In general, at least one other example of an embodiment provides a recurrent neural network (RNN) system and method that can communicate with and/or be driven by an orchestrator/scheduler to modify a computational cost of the RNN based on modifying a characteristic of the RNN, wherein the characteristic comprises a decision function of at least one cell of the RNN, and the decision function comprises determining whether to update the at least one cell of the RNN, modifying the characteristic comprises modifying at least one parameter of the decision function, and the at least one parameter comprises a binarization function associated with one or more perceptrons of the RNN.
In general, at least one other example of an embodiment provides a recurrent neural network (RNN) system and method that can communicate with and/or be driven by an orchestrator/scheduler to modify a computational cost of the RNN based on adjusting a value of at least one parameter of the RNN, wherein the at least one parameter comprises fbinarize associated with one or more perceptrons of the RNN. In general, at least one example of an embodiment can involve apparatus comprising one or more processors configured to implement a neural network including a decision function; receive an indication of a resource availability; adapt the neural network based on the indication, wherein the one or more processors being configured to adapt the neural network comprises the one or more processors being configured to modify the decision function based on the indication; and process data utilizing the adapted neural network in accordance with the resource availability.
In general, at least one other example of an embodiment involves a method comprising: receiving an indication of a resource availability; adapting a neural network based on the indication, wherein adapting the neural network comprises modifying a decision function associated with the neural network based on the indication; and processing data utilizing the adapted neural network in accordance with the resource availability.
In general, at least one other example of an embodiment can involve an apparatus or method including a neural network as described herein, wherein the neural network comprises a recurrent neural network.
In general, at least one other example of an embodiment can involve an apparatus or method including a recurrent neural network as described herein, wherein the recurrent neural network comprises a skip neural network.
In general, at least one other example of an embodiment can involve an apparatus or method receiving an indication, wherein the indication is received from an orchestrator.
In general, at least one other example of an embodiment can involve an apparatus or method including adapting a neural network, wherein the adapting occurs during training of the neural network.
In general, at least one other example of an embodiment can involve an apparatus or method including adapting a neural network during training, wherein adapting during training comprises varying a parameter for each of a plurality of minibatches of data during training.
In general, at least one other example of an embodiment can involve an apparatus or method including a neural network adapted based on varying a parameter, wherein the parameter comprises a variable parameter varied by an orchestrator based on resource availability.
In general, at least one other example of an embodiment can involve an apparatus or method including a neural network adapted by varying a parameter, wherein the parameter comprises a variable parameter and the variable parameter is varied based on determining a computational cost associated with adapting the neural network.
In general, at least one other example of an embodiment can involve an apparatus or method including a neural network and determining a computational cost associated with adapting the neural network, wherein determining the computational cost comprises evaluating the computational cost using a machine learning model. In general, at least one other example of an embodiment can involve an apparatus or method including a neural network adapted based on determining a computational cost associated with varying a parameter, wherein determining the computational cost comprises providing information to an orchestrator regarding a behavior of the neural network and processing the information by the orchestrator to determine the parameter.
In general, at least one other example of an embodiment can involve an apparatus or method including a neural network adapted based on providing information to an orchestrator, wherein providing the information to the orchestrator comprises providing metadata including the information to the orchestrator.
In general, at least one other example of an embodiment can involve an apparatus or method including a neural network adapted by varying a parameter of a binarization function, wherein the parameter of the binarization function comprises a threshold value at which the binarization function value switches between 0 and 1.
In general, at least one example of an embodiment can involve a computer program product including instructions, which, when executed by a computer, cause the computer to carry out any one or more of the methods described herein.
In general, at least one example of an embodiment can involve a non-transitory computer readable medium storing executable program instructions to cause a computer executing the instructions to perform any one or more of the methods described herein.
In general, at least one example of an embodiment can involve a device comprising an apparatus according to any embodiment of apparatus as described herein, and at least one of (i) an antenna configured to receive a signal, the signal including data representative of information such as instructions from an orchestrator, (ii) a band limiter configured to limit the received signal to a band of frequencies that includes the data representative of the information, and (iii) a display configured to display an image such as a displayed representation of the data representative of the instructions.
In general, at least one example of an embodiment can involve a device as described herein, wherein the device comprises one of a television, a television signal receiver, a set-top box, a gateway device, a mobile device, a cell phone, a tablet, or other electronic device.
Regarding the various embodiments described herein and the figures illustrating various embodiments, when a figure is presented as a flow diagram, it should be understood that it also provides a block diagram of a corresponding apparatus. Similarly, when a figure is presented as a block diagram, it should be understood that it also provides a flow diagram of a corresponding method/process.
The implementations and aspects described herein can be implemented in, for example, a method or a process, an apparatus, a software program, a data stream, or a signal. Even if only discussed in the context of a single form of implementation (for example, discussed only as a method), the implementation of features discussed can also be implemented in other forms (for example, an apparatus or program). An apparatus can be implemented in, for example, appropriate hardware, software, and firmware. The methods can be implemented in, for example, a processor, which refers to processing devices in general, including, for example, one or more of a computer, a microprocessor, an integrated circuit, or a programmable logic device. Processors also include communication devices, such as, for example, computers, cell phones, portable/personal digital assistants (“PDAs”), and other devices that facilitate communication of information between end-users.
Reference to “one embodiment” or “an embodiment” or “one implementation” or “an implementation”, as well as other variations thereof, means that a particular feature, structure, characteristic, and so forth described in connection with the embodiment is included in at least one embodiment. Thus, the appearances of the phrase “in one embodiment” or “in an embodiment” or “in one implementation” or “in an implementation”, as well any other variations, appearing in various places throughout this document are not necessarily all referring to the same embodiment.
Additionally, this document may refer to “obtaining” various pieces of information. Obtaining the information can include one or more of, for example, determining the information, estimating the information, calculating the information, predicting the information, or retrieving the information from memory.
Further, this document may refer to “accessing” various pieces of information. Accessing the information can include one or more of, for example, receiving the information, retrieving the information (for example, from memory), storing the information, moving the information, copying the information, calculating the information, determining the information, predicting the information, or estimating the information.
Additionally, this document may refer to “receiving” various pieces of information. Receiving is, as with “accessing”, intended to be a broad term. Receiving the information can include one or more of, for example, accessing the information, or retrieving the information (for example, from memory). Further, “receiving” is typically involved, in one way or another, during operations such as, for example, storing the information, processing the information, transmitting the information, moving the information, copying the information, erasing the information, calculating the information, determining the information, predicting the information, or estimating the information.
It is to be appreciated that the use of any of the following “/”, “and/or”, and “at least one of”, for example, in the cases of “A/B”, “A and/or B” and “at least one of A and B”, is intended to encompass the selection of the first listed option (A) only, or the selection of the second listed option (B) only, or the selection of both options (A and B). As a further example, in the cases of “A, B, and/or C” and “at least one of A, B, and C”, such phrasing is intended to encompass the selection of the first listed option (A) only, or the selection of the second listed option (B) only, or the selection of the third listed option (C) only, or the selection of the first and the second listed options (A and B) only, or the selection of the first and third listed options (A and C) only, or the selection of the second and third listed options (B and C) only, or the selection of all three options (A and B and C). This may be extended, as is clear to one of ordinary skill in this and related arts, for as many items as are listed.
Also, as used herein, the word “signal” refers to, among other things, indicating something to a corresponding decoder. For example, in certain embodiments the encoder signals a particular one of a plurality of parameters for refinement. In this way, in an embodiment the same parameter is used at both the encoder side and the decoder side. Thus, for example, an encoder can transmit (explicit signaling) a particular parameter to the decoder so that the decoder can use the same particular parameter. Conversely, if the decoder already has the particular parameter as well as others, then signaling can be used without transmitting (implicit signaling) to simply allow the decoder to know and select the particular parameter. By avoiding transmission of any actual functions, a bit savings is realized in various embodiments. It is to be appreciated that signaling can be accomplished in a variety of ways. For example, one or more syntax elements, flags, and so forth are used to signal information to a corresponding decoder in various embodiments. While the preceding relates to the verb form of the word “signal”, the word “signal” can also be used herein as a noun.
As will be evident to one of ordinary skill in the art, implementations can produce a variety of signals formatted to carry information that can be, for example, stored or transmitted. The information can include, for example, instructions for performing a method, or data produced by one of the described implementations. For example, a signal can be formatted to carry the bitstream or signal of a described embodiment. Such a signal can be formatted, for example, as an electromagnetic wave (for example, using a radio frequency portion of spectrum) or as a baseband signal. The formatting can include, for example, encoding a data stream and modulating a carrier with the encoded data stream. The information that the signal carries can be, for example, analog or digital information. The signal can be transmitted over a variety of different wired or wireless links, as is known. The signal can be stored on a processor-readable medium.
Various embodiments have been described. Embodiments may include any of the following features or entities, alone or in any combination, across various different claim categories and types:
Various other generalized, as well as particularized embodiments are also supported and contemplated throughout this disclosure.
| Number | Date | Country | Kind |
|---|---|---|---|
| 20305274.1 | Mar 2020 | EP | regional |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2021/056303 | 3/12/2021 | WO |
