摘要： 针对拓宽神经网络的结构会导致计算量增大，计算性能降低，需要针对并行的网络进行更有效的优化以及调度。通过分析FPGA平台上实现卷积神经网络的计算吞吐量和所需的带宽，在计算资源和访存带宽的限制下，采用了屋顶模型进行了设计空间的探索，提出了在不同支的卷积神经网络中使用不同的循环展开因子，从而实现同一卷积层中不同支神经网络的并行计算，保证计算资源和内存资源的合理分配。实验结果表明，所提出的设计与先前研究相比获得了1.31×的性能提升。

中图分类号： TN409
文献标识码： A
DOI：10.16157/j.issn.0258-7998.211279
中文引用格式： 谢思璞，魏榕山. 多分支卷积神经网络的FPGA设计与优化[J].电子技术应用，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