Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 4
1070-1076

CNN data quantization method based on reconfigurable array

Zhu Jiayanga
Jiang Linb
Li Yuanchengb
Song Jiac
Liu Shuaia
a. School of Communication & Information Engineering, b. School of Computer Science & Technology, c. School of Electrical & Control Engineering, Xi'an University of Science & Technology, Xi'an 710600, China
DOI: 10.19734/j.issn.1001-3695.2023.07.0378

Abstract

Convolution operations lead to a significant increase in the network size, which makes CNN models difficult to deploy to the embedded hardware platform, and different granularity data is not coordinated with the underlying hardware structure, which leads to low computing efficiency. Based on the reconfigurable array with the computing units supporting multiple bit widths, through software hardware cooperation and reconfigurable computing methods, this paper defined the quantization threshold using KL divergence and random integer method, proposed a strategy for finding the best basis point, designed an instruction set and a parallel mapping scheme supporting multiple bit widths to realize three distinct bit widths in data quantization. The results show the quantization scheme with 8 bit weight and feature map can compress model parameter quantity to about 50% with 2% accuracy loss. The acceleration ratios of quantifying the test images to three different bit widths reach 1.012, 1.273, and 1.556, respectively, which can shorten the execution time by up to 35.7% and reduce memory access times by 56.2%, while only bringing less than 1% relative error. This indicates that this method can achieve efficient neural network computation under three quantization bit widths, thereby implementing hardware acceleration and model compression.

Foundation Support

科技创新2030-“新一代人工智能”重大项目(2022ZD0119005)
国家自然科学基金重点资助项目(61834005)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.07.0378
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 4
Section: Algorithm Research & Explore
Pages: 1070-1076
Serial Number: 1001-3695(2024)04-017-1070-07

Publish History

[2023-11-03] Accepted Paper
[2024-04-05] Printed Article

Cite This Article

朱家扬, 蒋林, 李远成, 等. 基于可重构阵列的CNN数据量化方法 [J]. 计算机应用研究, 2024, 41 (4): 1070-1076. (Zhu Jiayang, Jiang Lin, Li Yuancheng, et al. CNN data quantization method based on reconfigurable array [J]. Application Research of Computers, 2024, 41 (4): 1070-1076. )

About the Journal

  • 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.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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