Convolutional neural network accelerator based on CPU-FPGA memory sharing

Abstract

The invention discloses a convolutional neural network accelerator based on CPU-FPGA memory sharing. A CPU processing subsystem comprises an input control module, a configuration parameter generationmodule and an output control module; the input control module receives and caches the pixel data and the weight data; the configuration parameter generation module controls configuration parameters; the output control module controls data transmission; the FPGA acceleration subsystem comprises an on-chip storage module, a calculation engine module and a control module; the on-chip storage module is used for buffering, reading and writing access of data; the calculation engine module accelerates calculation; and the control module controls the on-chip storage module to perform read-write operation on the data, and performs data exchange and calculation control with the calculation engine module. According to the method, the characteristics of high parallelism, high throughput rate and low power consumption of the FPGA can be brought into full play, and meanwhile, the flexible and efficient data processing characteristics of the CPU processor can be fully utilized, so that the whole system can efficiently and quickly realize the reasoning process of the convolutional neural network with relatively low power consumption.

Description

technical field [0001] The invention relates to the technical field of neural networks, and more specifically relates to a convolutional neural network accelerator based on CPU-FPGA memory sharing. Background technique [0002] Convolutional neural network is a kind of feed-forward neural network with convolution calculation and deep structure, and it is one of the representative algorithms of deep learning. The convolutional neural network has the ability of representation learning, and can perform translation invariant classification on the input information according to its hierarchical structure. With the introduction of deep learning theory and the improvement of numerical computing equipment, convolutional neural networks have developed rapidly and are widely used in computer vision, natural language processing and other fields. [0003] Due to the complexity of convolutional neural network calculations, general-purpose CPUs can no longer meet the computing needs. Exi...

AI Technical Summary

Problems solved by technology

[0005] In view of this, the present invention provides a convolutional neural network accelerator based on CPU-FPGA memory sharing, which solves the technical problem of applying convolutional neural networks to embedded systems with limited power consumption, and provides a solution for low-power consumption application scenarios. Convolutional Neural Networks Provide New Ways to Accelerate Computing

Images