FPGA-Based Accelerator for Rank-Enhanced and Highly-Pruned Block-Circulant Neural Networks
- Title
- FPGA-Based Accelerator for Rank-Enhanced and Highly-Pruned Block-Circulant Neural Networks
- Authors
- Song, Haena; Yoon, Jongho; Kim, Dohun; Kwon, Eunji; Oh, Tae-Hyun; Kang, Seokhyeong
- Date Issued
- 2023-04-17
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Abstract
- Numerous network compression methods have been proposed to deploy deep neural networks in a resource-constrained embedded system. Among them, block-circulant matrix (BCM) compression is one of the promising hardware-friendly methods for both acceleration and compression. However, it has several limitations; (i) limited representation due to the structural characteristic of circulant matrix, (ii) limitation of the compression parameter, (iii) need to specialize the dataflow for BCM-compressed network accelerators. In this paper, rank-enhanced and highly-pruned block-circulant matrices compression (RP-BCM) framework is proposed to overcome these limitations. RP-BCM comprises two stages: Hadamard-BCM and BCM-wise pruning. Moreover, a dedicated skip scheme is introduced to processing element design for exploiting high-parallelism with BCM-wise sparsity. Furthermore, we propose specialized dataflow for a BCM-compressed network on a resource-constrained FPGA. As a result, the proposed method achieves parameter reduction and FLOPs reduction for ResNet-50 in ImageNet by 92.4% and 77.3%, respectively. Moreover, the proposed hardware design achieves 3.1× improvement in energy efficiency on the Xilinx PYNQ-Z2 FPGA board for ResNet-18 on ImageNet compared to the GPU.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/122627
- Article Type
- Conference
- Citation
- 2023 Design, Automation and Test in Europe Conference and Exhibition, DATE 2023, 2023-04-17
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