关键词: CNN加速器, 现场可编程门阵列（FPGA）, 高层次综合（HLS）, 存储优化, 定点量化

Abstract:

The rapid development of computer vision requires higher and higher system performance of embedded products, traditional Field Programmable Gate Array（FPGA） platform has some problems that computational throughput does not match the memory bandwidth well, the implementation efficiency of general processor pair Convolutional Neural Network（CNN） is not high, and the performance requirements are not met. Aiming at above design bottlenecks, using the classic LeNet-5 neural network model, a high-performance face recognition neural network accelerator is designed on the Xilinx ZC706 embedded development platform, which is optimized by storage based on High Level Synthesis（HLS） tools. The fixed-point quantization, computational optimization and other aspects of the neural network model are optimized and improved, and the 7-layer CNN accelerator is realized. Experimental results show that the operating frequency of CNN accelerator is 200 MHz. Compared with the CPU, the accelerator achieves 126 times acceleration, which is more than ten times faster than the GPU speed, and the power consumption is only 2.62 W.

Key words: CNN accelerator, Field Programmable Gate Array（FPGA）, High Level Synthesis（HLS）, storage optimization, fixed point quantization