中圖分類(lèi)號(hào)： TN409
文獻(xiàn)標(biāo)識(shí)碼： A
DOI：10.16157/j.issn.0258-7998.211279
中文引用格式： 謝思璞，魏榕山. 多分支卷積神經(jīng)網(wǎng)絡(luò)的FPGA設(shè)計(jì)與優(yōu)化[J].電子技術(shù)應(yīng)用，2021，47(7)：97-101.
英文引用格式： Xie Sipu，Wei Rongshan. FPGA design and optimization of multi-branch CNN[J]. Application of Electronic Technique，2021，47(7)：97-101.

FPGA design and optimization of multi-branch CNN

Abstract： Broadening the structure of the neural network will lead to the increase of the amount of computation and the decrease of the computational performance, it is necessary to optimize and schedule the parallel network more effectively. By analyzing the throughput and bandwidth of convolutional neural network on FPGA platform, the roof model is used to explore the design space under the limitation of computing resources and memory access bandwidth. It is proposed to use different cycle expansion factors in different branches of convolutional neural network, so as to realize the parallel computing of different branches of neural network in the same convolution layer and ensure the computing efficiency reasonable allocation of resources and memory resources. The experimental results show that the performance of the proposed design is improved by 1.31×compared with the previous research.

Key words : multi-branch convolutional neural network；FPGA；roofline model；parallel computing