System Development & Application
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 1
172-177,182

Design and scheduling of convolutional neural network accelerator for cloud FPGA

Cai Ruichu1
Yu Yang1
Zhong Chunrong1
Lu Ye2
Chen Yao1,3
1. College of Computers, Guangdong University of Technology, Guangzhou 510006, China
2. College of Computer & Control Engineering, Nankai University, Tianjin 300350, China
3. Advanced Digital Sciences Center, Singapore 138602, Singapore
DOI: 10.19734/j.issn.1001-3695.2018.05.0507

Abstract

Convolutional neural network's high computational complexity often obstructs its widespread adhibition in real-time and low-power applications. The existing software implementation solution cannot meet the demands of the CNN for computing performance and power consumption. The traditional FPGA-oriented CNN construction method has problems such as complicated process, long cycle and small optimization space. For these problems, according to the characteristics of CNN calculation pattern, this paper proposed a design and scheduling mechanism of convolutional neural network accelerator for cloud FPGA. By using for reference the design which based HLS technology, importing the cyclic cutting parameters and rearranging the convolution layer circularly, it constructed the network in a modular way, and extended parameters to further optimize the accelerator processing process. It summarized the scheduling scheme by analyzing the characteristics of system tasks and resources, and optimized its design from two aspects of control and data flow. In comparison with other existing works, the proposed design provided a solution with flexibility, low energy consumption, high energy efficiency and performance. The design also discussed the efficient universal scheduling scheme of the accelerator. Experimental results show that the accelerator can improve the computing speed and reduce the power consumption.

Foundation Support

NSFC-广东联合基金资助项目(U1501254)
广东省杰出青年科学基金资助项目(2014A030306004)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.05.0507
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 1
Section: System Development & Application
Pages: 172-177,182
Serial Number: 1001-3695(2020)01-036-0172-06

Publish History

[2020-01-05] Printed Article

Cite This Article

蔡瑞初, 余洋, 钟椿荣, 等. 面向云端FPGA的卷积神经网络加速器的设计及其调度 [J]. 计算机应用研究, 2020, 37 (1): 172-177,182. (Cai Ruichu, Yu Yang, Zhong Chunrong, et al. Design and scheduling of convolutional neural network accelerator for cloud FPGA [J]. Application Research of Computers, 2020, 37 (1): 172-177,182. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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