Xilinx, Inc.

Ashish Sirasao

Elliott Delaye

Aaron Ng

Jindrich Zejda

Ehsan Ghasemi

Yongjun Wu

Bing Tian

Xiao Teng

Krishna Garlapati

Chaithanya Dudha

Sean Settle

Shangzhi Sun

Satyaprakash Pareek

Alireza S Kaviani

Khang Kim Dao

Aman Gayasen

Nithin Kumar Guggilla

Ling Liu

Anup Hosangadi

Zhao Ma

Yifei Zhou

Christopher H Dick

Ralph D Wittig

Raymond Kong

Sonal Santan

Michael Wu

Jun Liu

Ilya K Ganusov

Henri Fraisse

Soren T Soe

Brian S Martin

Hao Yu

Sumanta Datta

Yao Fu

Rajeev Patwari

Venkata V Dhanikonda

Christopher J Case

Surya Pratik Saha

Mujib Haider

Jay Southard

Yinyi Wang

Elliot Delaye

Chuanhua Song

Oscar Fernando C Fernandez

Embodiments herein describe techniques for expressing the layers of a neural network in a software model. In one embodiment, the software model includes a class that describes the various functional blocks (e.g., convolution units, max-pooling units, rectified linear units (ReLU), and scaling functions) used to execute the neural network layers. In turn, other classes in the software model can describe the operation of each of the functional blocks. In addition, the software model can include conditional logic for expressing how the data flows between the functional blocks since different layers in the neural network can process the data differently. A compiler can convert the high-level code in the software model (e.g., C++) into a hardware description language (e.g., register transfer level (RTL)) which is used to configure a hardware system to implement a neural network accelerator.

Software defined neural network layer pipelining

Embodiments herein describe techniques for static scheduling a neural network implemented in a massively parallel hardware system. The neural network may be scheduled using three different scheduling levels referred to herein as an upper level, an intermediate level, and a lower level. In one embodiment, the upper level includes a hardware or software model of the layers in the neural network that establishes a sequential order of functions that operate concurrently in the hardware system. In the intermediate level, identical processes in the functions defined in the upper level are connected to form a systolic array or mesh and balanced data flow channels are used to minimize latency. In the lower level, a compiler can assign the operations performed by the processing elements in the systolic array to different portions of the hardware system to provide a static schedule for the neural network.

Static block scheduling in massively parallel software defined hardware systems

Embodiments herein describe techniques for interfacing a neural network application with a neural network accelerator using a library. The neural network application may execute on a host computing system while the neural network accelerator executes on a massively parallel hardware system, e.g., a FPGA. The library operates a pipeline for submitting the tasks received from the neural network application to the neural network accelerator. In one embodiment, the pipeline includes a pre-processing stage, an FPGA execution stage, and a post-processing stage which each correspond to different threads. When receiving a task from the neural network application, the library generates a packet that includes the information required for the different stages in the pipeline to perform the tasks. Because the stages correspond to different threads, the library can process multiple packets in parallel which can increase the utilization of the neural network accelerator on the hardware system.

Machine learning runtime library for neural network acceleration

In the disclosed methods and systems for processing in a neural network system, a host computer system writes a plurality of weight matrices associated with a plurality of layers of a neural network to a memory shared with a neural network accelerator. The host computer system further assembles a plurality of per-layer instructions into an instruction package. Each per-layer instruction specifies processing of a respective layer of the plurality of layers of the neural network, and respective offsets of weight matrices in a shared memory. The host computer system writes input data and the instruction package to the shared memory. The neural network accelerator reads the instruction package from the shared memory and processes the plurality of per-layer instructions of the instruction package.

Multi-layer neural network processing by a neural network accelerator using host communicated merged weights and a package of per-layer instructions

A disclosed neural network processing system includes a host computer system, a RAMs coupled to the host computer system, and neural network accelerators coupled to the RAMs, respectively. The host computer system is configured with software that when executed causes the host computer system to write input data and work requests to the RAMS. Each work request specifies a subset of neural network operations to perform and memory locations in a RAM of the input data and parameters. A graph of dependencies among neural network operations is built and additional dependencies added. The operations are partitioned into coarse grain tasks and fine grain subtasks for optimal scheduling for parallel execution. The subtasks are scheduled to accelerator kernels of matching capabilities. Each neural network accelerator is configured to read a work request from the respective RAM and perform the subset of neural network operations on the input data using the parameters.

Neural network processing system having host controlled kernel acclerators

Time-multiplexing implementation of hardware accelerated functions includes associating each function of a plurality of functions from program code with an accelerator binary image specifying a hardware accelerated version of the associated function and determining which accelerator binary images are data independent. Using the computer hardware, the accelerator binary images can be scheduled for implementation in a programmable integrated circuit within each of a plurality of partial reconfiguration regions based on data independence.

Time-multiplexed implementation of hardware accelerated functions in a programmable integrated circuit

In disclosed approaches of neural network processing, a host computer system copies an input data matrix from host memory to a shared memory for performing neural network operations of a first layer of a neural network by a neural network accelerator. The host instructs the neural network accelerator to perform neural network operations of each layer of the neural network beginning with the input data matrix. The neural network accelerator performs neural network operations of each layer in response to the instruction from the host. The host waits until the neural network accelerator signals completion of performing neural network operations of layer i before instructing the neural network accelerator to commence performing neural network operations of layer i+1, for i&#x2265;1. The host instructs the neural network accelerator to use a results data matrix in the shared memory from layer i as an input data matrix for layer i+1 for i&#x2265;1.

Host-directed multi-layer neural network processing via per-layer work requests

An example preprocessor circuit includes: a first buffer configured to store rows of image data and output a row thereof; a second buffer, coupled to the first buffer, including storage locations to store respective image samples of the row output by the first buffer; shift registers; an interconnect network including connections, each connection coupling a respective one of the shift registers to more than one of the storage locations, one or more of the storage locations being coupled to more than one of the connections; and a control circuit configured to load the shift registers with the image samples based on the connections and shift the shift registers to output streams of image samples.

Image preprocessing for generalized image processing

At least one neural network accelerator performs operations of a first subset of layers of a neural network on an input data set, generates an intermediate data set, and stores the intermediate data set in a shared memory queue in a shared memory. A first processor element of a host computer system provides input data to the neural network accelerator and signals the neural network accelerator to perform the operations of the first subset of layers of the neural network on the input data set. A second processor element of the host computer system reads the intermediate data set from the shared memory queue, performs operations of a second subset of layers of the neural network on the intermediate data set, and generates an output data set while the neural network accelerator is performing the operations of the first subset of layers of the neural network on another input data set.

Neural network processing system having multiple processors and a neural network accelerator

Embodiments herein describe techniques for interfacing a neural network application with a neural network accelerator that operate on two heterogeneous computing systems. For example, the neural network application may execute on a central processing unit (CPU) in a computing system while the neural network accelerator executes on a FPGA. As a result, when moving a software-hardware boundary between the two heterogeneous systems, changes may be made to both the neural network application (using software code) and to the accelerator (using RTL). The embodiments herein describe a software defined approach where shared interface code is used to express both sides of the interface between the two heterogeneous systems in a single abstraction (e.g., a software class).

Re-targetable interface for data exchange between heterogeneous systems and accelerator abstraction into software instructions

Determining on-chip memory access patterns can include modifying a circuit design to include a profiler circuit for a random-access memory (RAM) of the circuit design, wherein the profiler circuit is configured to monitor an address bus of the RAM, and modifying the circuit design to include a debug circuit connected to the profiler circuit. Usage data for the RAM can be generated by detecting, using the profiler circuit, addresses of the RAM accessed during a test of the circuit design, as implemented in an integrated circuit. The usage data for the RAM can be output using the debug circuit.

On-chip memory access pattern detection for power and resource reduction

A circuit arrangement includes a buffer, a height traversal circuit configured to generate a sequence of IFM height values in response to first control signals, a width traversal circuit configured to generate a sequence of IFM width values in response to second control signals, a control circuit, and an address generation circuit. The control circuit is configured to input an OFM height, an OFM width, a kernel height, and a kernel width; generate the first control signals at times based on the OFM height and the kernel height; and generate the second control signals at times based on the OFM width and the kernel width. The address generation circuit is configured to generate a sequence of addresses based on the sequences of IFM height values and IFM width values, provide the sequence of addresses to the buffer, and enable reading from the buffer.

Circuit arrangements and methods for traversing input feature maps

Methods and apparatus are described for simultaneously buffering and reformatting (e.g., transposing) a matrix for high-speed data streaming in general matrix multiplication (GEMM), which may be implemented by a programmable integrated circuit (IC). Examples of the present disclosure increase the effective double data rate (DDR) memory throughput for streaming data into GEMM digital signal processing (DSP) engine multifold, as well as eliminate slow data reformatting on a host central processing unit (CPU). This may be accomplished through software-defined (e.g., C++) data structures and access patterns that result in hardware logic that simultaneously buffers and reorganizes the data to achieve linear DDR addressing.

Software-defined buffer/transposer for general matrix multiplication in a programmable IC

Implementing a circuit design can include detecting, using computer hardware, a re-convergent section of a circuit design, masking, using the computer hardware, a sequential circuit element of the re-convergent section located between a start and an end of the re-convergent section, and performing, using the computer hardware, an optimization operation on combinatorial logic of the re-convergent section to create optimized combinatorial logic. Using the computer hardware, the optimized combinatorial logic of the re-convergent section can be mapped. Further, the re-convergent section can be modified subsequent to the mapping to match timing of the re-convergent section prior to the masking.

Implementing a circuit design with re-convergence

Detecting objects in video may include receiving object detections for a plurality of selected frames of a video from a still image detector, wherein the plurality of selected frames are non-adjacent frames of the video, propagating the object detections from the plurality of selected frames to sequential frames of the video adjacent to the plurality of selected frames based on a distance metric and vector flow data for the sequential frames, suppressing false positive object detections from the propagating, and outputting resulting object detections for the sequential frames of the video.

Object detection in video

Growing community of inventors

San Jose, CA, United States of America

Ashish Sirasao