App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 6

Hybrid-grainularity CNN sparsification with efficient deployment on reconfigurable architectures

Hao Juan1a
Jiang Lin2
Li Yuancheng1b
Liu Dongyue1c
1. a. College of Communication and Information Technology, b. College of Artificial Intelligence and Computer Science, c. School of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an 710600, China
2. College of Electronic Information, Northwest University, Xi'an 710127, China
DOI: 10.19734/j.issn.1001-3695.2025.09.0425

Abstract

Convolutional neural networks (CNNs) demonstrate outstanding performance in image recognition tasks, yet their high computational and storage demands constrain deployment on edge computing platforms. Reconfigurable architectures, characterised by flexibility and efficiency, provide the hardware foundation for optimising CNN computation and memory access. To address these challenges, this study proposed a hybrid-granularity CNN sparsification method tailored for reconfigurable architectures. The method first applied coarse-grained filter sparsification based on feature-map rank information. It then grouped the retained convolutional kernels and uniformly pruned channel stripes at identical spatial positions within each group to realise fine-grained sparsification, and finally implemented parallelisation strategies on the reconfigurable hardware platform. Validation on the CIFAR-10 dataset using the ResNet-56 model demonstrates that the method reduces FLOPs by 46.8%, shortens inference latency to 18.94 ms, and delivers a 1.62× speedup with only a 0.18% loss in classification accuracy. It also achieves 94.5% average Processing Element (PE) utilisation and reduces off-chip DRAM accesses by 41.7%, thereby enhancing computational and memory efficiency in reconfigurable architectures.

Foundation Support

新一代人工智能国家科技重大专项(2022ZD0119005)
陕西省自然科学基础研究计划(2025JC-YBMS-754,2024JC-YBMS-539)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.09.0425
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 6

Publish History

[2026-02-25] Accepted Paper

Cite This Article

郝娟, 蒋林, 李远成, 等. 混合粒度CNN稀疏化方法及其在可重构架构上的高效部署 [J]. 计算机应用研究, 2026, 43 (6). (2026-02-25). https://doi.org/10.19734/j.issn.1001-3695.2025.09.0425. (Hao Juan, Jiang Lin, Li Yuancheng, et al. Hybrid-grainularity CNN sparsification with efficient deployment on reconfigurable architectures [J]. Application Research of Computers, 2026, 43 (6). (2026-02-25). https://doi.org/10.19734/j.issn.1001-3695.2025.09.0425. )

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.

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