1. Field of the Invention
The present invention generally relates to data transmissions, and, more specifically, to an approach for performing channel equalization training.
2. Description of the Related Art
A typical data connector, such as a peripheral component interface (PCI) or PCI express (PCIe), allows different processing units within a computer system to exchange data with one another. For example, a conventional computer system could include a central processing unit (CPU) that exchanges data with a graphics processing unit (GPU) across a PCIe bus.
When a signal is transmitted across the data connector on a transmission channel, some frequency components may be attenuated more than others, which can make the signal illegible at the receiving end. As transmission speeds get faster, the transmissions can become more prone to errors as the noise effects are more severe. In high-speed transmission channels, the signal quality is critically important. One technique to combat this tendency is to “equalize” the channel so that the frequency domain attributes of the signal at the input end are faithfully reproduced at the output end, resulting in fewer errors. High-speed serial communications protocols like PCIe use equalizers to prepare data signals for transmission.
Equalization can be performed on both the transmit end and the receive end of a channel. For transmit equalization, the signal can be reshaped at the transmit end before the signal is sent to attempt to overcome the distortion that will be introduced by the channel. At the receive end, the signal can be reconditioned to improve the signal quality.
For transmit equalization in PCIe, parameters known as equalization coefficients can be used to tune the transmitter. A typical system may have hundreds of combinations of equalization coefficients, and some of these combinations will produce better equalization results than others. The signal quality is critically important in high-speed transmission channels, so an optimal set of coefficients is crucial to ensure accurate transmissions.
High speed interfaces require these parameters to be automatically adapted based on the interface to have sufficient performance. Adjustments can be computed in the receiver and communicated to the transmitter via a back channel. Existing back channel adaptation schemes assume that the different transmission taps are adjusted independently. However, in systems subject to peak power constraints the taps cannot be adjusted independently. Multiple cursor tap weights have to be considered when adjusting the tap weights.
Accordingly, what is needed in the art is a technique that adjusts transmission tap weights when the tap weights are subject to peak amplitude or power constraints.
One embodiment of the present invention comprises adapting a first tap weight of an equalizer, wherein a second tap weight of the equalizer is based at least in part on the first tap weight. Adapting the first tap weight further comprises computing a gradient from a data signal, an error signal and a channel pulse response sample. Adapting the first tap weight also comprises filtering the gradient with a loop filter and sending information to a transmitter via a back channel. Adapting the first tap weight further comprises configuring the first tap weight based on the information.
Advantageously, selecting equalization coefficients using the above techniques allows for selection of coefficients that meet the quality criteria required by the system and that remain subject to the peak amplitude or power constraints. In addition, systems subject to peak power constraints may result in lower power consumption than other systems.
So that the manner in which the above recited features of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to embodiments, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only typical embodiments of this invention and are therefore not to be considered limiting of its scope, for the invention may admit to other equally effective embodiments.
In the following description, numerous specific details are set forth to provide a more thorough understanding of the present invention. However, it will be apparent to one of skill in the art that the present invention may be practiced without one or more of these specific details. In other instances, well-known features have not been described in order to avoid obscuring the present invention.
PPU 112 is configured to execute a software application, such as e.g. device driver 103, that allows PPU 112 to generate arbitrary packet types that can be transmitted across communication path 113. Those packet types are specified by the communication protocol used by communication path 113. In situations where a new packet type is introduced into the communication protocol (e.g., due to an enhancement to the communication protocol), PPU 112 can be configured to generate packets based on the new packet type and to exchange data with CPU 102 (or other processing units) across communication path 113 using the new packet type.
In one embodiment, the parallel processing subsystem 112 incorporates circuitry optimized for graphics and video processing, including, for example, video output circuitry, and constitutes a graphics processing unit (GPU). In another embodiment, the parallel processing subsystem 112 incorporates circuitry optimized for general purpose processing, while preserving the underlying computational architecture, described in greater detail herein. In yet another embodiment, the parallel processing subsystem 112 may be integrated with one or more other system elements, such as the memory bridge 105, CPU 102, and I/O bridge 107 to form a system on chip (SoC).
It will be appreciated that the system shown herein is illustrative and that variations and modifications are possible. The connection topology, including the number and arrangement of bridges, the number of CPUs 102, and the number of parallel processing subsystems 112, may be modified as desired. For instance, in some embodiments, system memory 104 is connected to CPU 102 directly rather than through a bridge, and other devices communicate with system memory 104 via memory bridge 105 and CPU 102. In other alternative topologies, parallel processing subsystem 112 is connected to I/O bridge 107 or directly to CPU 102, rather than to memory bridge 105. In still other embodiments, I/O bridge 107 and memory bridge 105 might be integrated into a single chip. Large embodiments may include two or more CPUs 102 and two or more parallel processing systems 112. The particular components shown herein are optional; for instance, any number of add-in cards or peripheral devices might be supported. In some embodiments, switch 116 is eliminated, and network adapter 118 and add-in cards 120, 121 connect directly to I/O bridge 107.
Referring again to
In operation, CPU 102 is the master processor of computer system 100, controlling and coordinating operations of other system components. In particular, CPU 102 issues commands that control the operation of PPUs 202. In some embodiments, CPU 102 writes a stream of commands for each PPU 202 to a pushbuffer (not explicitly shown in either
Referring back now to
In one embodiment, communication path 113 is a PCIe link, in which dedicated lanes are allocated to each PPU 202, as is known in the art. Other communication paths may also be used. As mentioned above, the contraflow interconnect may also be used to implement the communication path 113, as well as any other communication path within the computer system 100, CPU 102, or PPU 202. An I/O unit 205 generates packets (or other signals) for transmission on communication path 113 and also receives all incoming packets (or other signals) from communication path 113, directing the incoming packets to appropriate components of PPU 202. For example, commands related to processing tasks may be directed to a host interface 206, while commands related to memory operations (e.g., reading from or writing to parallel processing memory 204) may be directed to a memory crossbar unit 210. Host interface 206 reads each pushbuffer and outputs the work specified by the pushbuffer to a front end 212. Although not explicitly shown, communication links can be between a GPU and its dedicated memory, between a GPU and one or more other GPUs, between a GPU and a display, and/or between a CPU and various peripherals (e.g., PCI Express and USB).
Each PPU 202 advantageously implements a highly parallel processing architecture. As shown in detail, PPU 202(0) includes a processing cluster array 230 that includes a number C of general processing clusters (GPCs) 208, where C≧1. Each GPC 208 is capable of executing a large number (e.g., hundreds or thousands) of threads concurrently, where each thread is an instance of a program. In various applications, different GPCs 208 may be allocated for processing different types of programs or for performing different types of computations. For example, in a graphics application, a first set of GPCs 208 may be allocated to perform tessellation operations and to produce primitive topologies for patches, and a second set of GPCs 208 may be allocated to perform tessellation shading to evaluate patch parameters for the primitive topologies and to determine vertex positions and other per-vertex attributes. The allocation of GPCs 208 may vary dependent on the workload arising for each type of program or computation.
GPCs 208 receive processing tasks to be executed via a work distribution unit 200, which receives commands defining processing tasks from front end unit 212. Processing tasks include indices of data to be processed, e.g., surface (patch) data, primitive data, vertex data, and/or pixel data, as well as state parameters and commands defining how the data is to be processed (e.g., what program is to be executed). Work distribution unit 200 may be configured to fetch the indices corresponding to the tasks, or work distribution unit 200 may receive the indices from front end 212. Front end 212 ensures that GPCs 208 are configured to a valid state before the processing specified by the pushbuffers is initiated.
When PPU 202 is used for graphics processing, for example, the processing workload for each patch is divided into approximately equal sized tasks to enable distribution of the tessellation processing to multiple GPCs 208. A work distribution unit 200 may be configured to produce tasks at a frequency capable of providing tasks to multiple GPCs 208 for processing. By contrast, in conventional systems, processing is typically performed by a single processing engine, while the other processing engines remain idle, waiting for the single processing engine to complete its tasks before beginning their processing tasks. In some embodiments of the present invention, portions of GPCs 208 are configured to perform different types of processing. For example a first portion may be configured to perform vertex shading and topology generation, a second portion may be configured to perform tessellation and geometry shading, and a third portion may be configured to perform pixel shading in screen space to produce a rendered image. Intermediate data produced by GPCs 208 may be stored in buffers to allow the intermediate data to be transmitted between GPCs 208 for further processing.
Memory interface 214 includes a number D of partition units 215 that are each directly coupled to a portion of parallel processing memory 204, where D≧1. As shown, the number of partition units 215 generally equals the number of DRAM 220. In other embodiments, the number of partition units 215 may not equal the number of memory devices. Persons skilled in the art will appreciate that dynamic random access memories (DRAMs) 220 may be replaced with other suitable storage devices and can be of generally conventional design. A detailed description is therefore omitted. Render targets, such as frame buffers or texture maps may be stored across DRAMs 220, allowing partition units 215 to write portions of each render target in parallel to efficiently use the available bandwidth of parallel processing memory 204.
Any one of GPCs 208 may process data to be written to any of the DRAMs 220 within parallel processing memory 204. Crossbar unit 210 is configured to route the output of each GPC 208 to the input of any partition unit 215 or to another GPC 208 for further processing. GPCs 208 communicate with memory interface 214 through crossbar unit 210 to read from or write to various external memory devices. In one embodiment, crossbar unit 210 has a connection to memory interface 214 to communicate with I/O unit 205, as well as a connection to local parallel processing memory 204, thereby enabling the processing cores within the different GPCs 208 to communicate with system memory 104 or other memory that is not local to PPU 202. In the embodiment shown in
Again, GPCs 208 can be programmed to execute processing tasks relating to a wide variety of applications, including but not limited to, linear and nonlinear data transforms, filtering of video and/or audio data, modeling operations (e.g., applying laws of physics to determine position, velocity and other attributes of objects), image rendering operations (e.g., tessellation shader, vertex shader, geometry shader, and/or pixel shader programs), and so on. PPUs 202 may transfer data from system memory 104 and/or local parallel processing memories 204 into internal (on-chip) memory, process the data, and write result data back to system memory 104 and/or local parallel processing memories 204, where such data can be accessed by other system components, including CPU 102 or another parallel processing subsystem 112.
A PPU 202 may be provided with any amount of local parallel processing memory 204, including no local memory, and may use local memory and system memory in any combination. For instance, a PPU 202 can be a graphics processor in a unified memory architecture (UMA) embodiment. In such embodiments, little or no dedicated graphics (parallel processing) memory would be provided, and PPU 202 would use system memory exclusively or almost exclusively. In UMA embodiments, a PPU 202 may be integrated into a bridge chip or processor chip or provided as a discrete chip with a high-speed link (e.g., PCIe) connecting the PPU 202 to system memory via a bridge chip or other communication means.
As noted above, any number of PPUs 202 can be included in a parallel processing subsystem 112. For instance, multiple PPUs 202 can be provided on a single add-in card, or multiple add-in cards can be connected to communication path 113, or one or more of PPUs 202 can be integrated into a bridge chip. PPUs 202 in a multi-PPU system may be identical to or different from one another. For instance, different PPUs 202 might have different numbers of processing cores, different amounts of local parallel processing memory, and so on. Where multiple PPUs 202 are present, those PPUs may be operated in parallel to process data at a higher throughput than is possible with a single PPU 202. Systems incorporating one or more PPUs 202 may be implemented in a variety of configurations and form factors, including desktop, laptop, or handheld personal computers, servers, workstations, game consoles, embedded systems, and the like.
Back Channel Adaptation for Transmission Under Peak Power Constraints
The right side of
In PCI Express Gen 3, equalization comprises implementing settings that compensate for ISI to make the received signal look like the original transmitted signal. Both transmission and receive equalization may be performed. In transmission equalization, the signal at the transmit end is “reshaped” before the signal is sent, in a manner that is complementary to the distortion that will be introduced by the channel. In other words, the reshaping can counteract the distortion. Reshaping can allow the receive end to more easily differentiate between 1s and 0s. In receive equalization, the signal is reconditioned at the receive end to counter the distortion introduced by the channel and further improve the signal quality. In PCI Express Gen 3, both transmission and receive equalization can be performed.
O
A closed-form expression can be used to adjust a tap and minimize the mean-squared error. Computations based on this expression can be performed by embodiments of the present disclosure utilizing hardware and/or software. Various components of the present disclosure can perform the computations. Below is a derivation of expressions that can be used in certain embodiments of the present disclosure. For independent tap control, the expression is:
where p is the amplitude, k is the kth data bit, ek is an error for each kth bit (i.e., the difference between the ideal and the actual signal received), and d is the data bit. For transmissions with peak power or peak amplitude constraints (i.e., the taps are dependent on one another), the expression is:
As seen, the expression utilizes the amplitudes of multiple values of p (in this case, pi−l and pi. For the latch input:
pi−l denotes the pulse response samples of the link between the TXFIR output and the latch input (including the channels and the RX equalizers).
A peak power constraint can be computed with the expression:
where M is the peak amplitude.
The value of the summation determines whether to increase, decrease, or keep the tap weight the same. A channel can have three parameters: a precursor, post cursor, and main cursor. Each parameter has an associated lookup table. The gradient computed for each parameter informs the system to move up, move down, or stay at the same spot in the lookup table. These adjustments can continue over multiple transmissions to perform equalization on the channel. The expressions described above will adjust the tap weights while also respecting the peak power constraint.
In the examples discussed above, if cl is negative or zero,
c
l
=−ρ·M, l=1 or −1
c
0=(1−ρ)·M
The algorithm therefore only needs to adapt ρ.
In the first example,
r
k=Σi=−∞∞Σi=−10(cl·pi−l·dk−i=Σi=−∞∞{[1−ρ)·M·pi−ρ·M·pi+1]·dk−i}=i=∞∞{[pi+ρ·(−pi+1−pi)]·M·dk−i}
The error can be defined as:
e
k
=r
k
−h
0·
If we differentiate ek2 with ρ, the result is:
The constant factor may be dropped in implementations with adjustable loop bandwidth. Consequently, the gradient is:
For the second example, going through similar analysis, one can get:
Or more generally,
where l=1, −1 or another number associated with the other tap to be adjusted. In certain embodiments, l=1 for the post cursor, 0 for the main cursor, and −1 for the precursor.
As shown, a method 1200 begins in step 1210, where a gradient is computed from a data signal, an error signal, and a channel pulse response sample. One example embodiment for computing this gradient is described above. In step 1220, the gradient is filtered with a loop filter. In step 1230, information is sent to a transmitter via a back channel. In some embodiments, the information is computed by one or more units within a receiver and then sent to the transmitter. The information may include, for example, an adjustment to a tap weight. In some embodiments, the information may be derived at least in part from data found in a lookup table. In other embodiments, the information may be a code corresponding to the first tap weight. In step 1240, the first tap weight is configured based on the information.
In summary, a new adaptation algorithm is used to adjust the transmission tap weights when they are subject to peak amplitude or power constraints. A transmitter may comprise one or more transmission taps, which need to be adjusted for equalization but cannot be adjusted independently. The algorithms described above can compute a gradient to adjust the tap weights. The adjustments can be derived from a lookup table. The adjustments can be sent from a receiver to the transmitter via a back channel. Adjustments in accordance with the present disclosure allow the tap weights to converge to the optimal setting in a timely manner while respecting the peak power or amplitude constraints. Advantageously, a system utilizing this algorithm may consume less power than existing systems.
One embodiment of the invention may be implemented as a program product for use with a computer system. The program(s) of the program product define functions of the embodiments (including the techniques described herein) and can be contained on a variety of computer-readable storage media. Illustrative computer-readable storage media include, but are not limited to: (i) non-writable storage media (e.g., read-only memory devices within a computer such as CD-ROM disks readable by a CD-ROM drive, flash memory, ROM chips or any type of solid-state non-volatile semiconductor memory) on which information is permanently stored; and (ii) writable storage media (e.g., floppy disks within a diskette drive or hard-disk drive or any type of solid-state random-access semiconductor memory) on which alterable information is stored.
The invention has been described above with reference to specific embodiments. Persons skilled in the art, however, will understand that various modifications and changes may be made thereto without departing from the broader spirit and scope of the invention as set forth in the appended claims. The foregoing description and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense.