中圖分類(lèi)號(hào)： TN108.1 文獻(xiàn)標(biāo)識(shí)碼： A DOI：10.16157/j.issn.0258-7998.200841 中文引用格式： 程佳風(fēng)，王紅亮. 基于HLS工具的CNN加速器的設(shè)計(jì)與優(yōu)化方法研究[J].電子技術(shù)應(yīng)用，2021，47(3)：18-21，26. 英文引用格式： Cheng Jiafeng，Wang Hongliang. Research on the design and optimization method of CNN accelerator based on HLS tools[J]. Application of Electronic Technique，2021，47(3)：18-21，26.
Research on the design and optimization method of CNN accelerator based on HLS tools
Cheng Jiafeng，Wang Hongliang
National Key Laboratory for Electronic Measurement Technology，North University of China，Taiyuan 030051，China
Abstract： Based on the idea of software and hardware co-design, this article uses HLS tools to design and implement a convolutional neural network accelerator on the PYNQ-Z2 platform, and uses the matrix cutting optimization method for convolution operations to balance resource consumption and computing resources , so that the performance of the accelerator is optimized. This article uses the MNIST data set to test the performance of the accelerator IP core. The experimental results show that: for a single image test, the accelerator achieves an acceleration effect of 5.785 compared with the ARM platform, and an acceleration of 9.72 for a 1000 image test. As a result, as the number of test images continues to increase, the performance of the accelerator will become better and better.
Key words : convolutional neural network(CNN)；PYNQ-Z2；HLS tool；accelerator
