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随着脉冲神经网络(Spiking Neural Network,SNN)在硬件部署优化方面的发展，基于现场可编程门阵列(Field-Programmable Gate Array,FPGA)的SNN处理器因其高效性与灵活性成为研究热点。然而，现有方法依赖多时间步训练和可重配置计算架构，增加了计算与存储压力，降低了部署效率。本文设计并实现了一种高能效、轻量化的残差SNN硬件加速器，采用算法与硬件协同设计策略，以优化SNN推理过程中的能效表现。在算法上，采用单时间步训练方法，并引入分组卷积和批归一化(Batch Normalization,BN)层融合技术，有效压缩网络规模至0.69M。此外，采用量化感知训练(Quantization-Aware Training,QAT)，将网络参数精度限制为8 bit。在硬件设计上，本文通过层内资源复用提高FPGA资源利用率，采用全流水层间架构提升计算吞吐率，并利用块随机存取存储器(Block Random Access Memory,BRAM)存储网络参数和计算结果，以提高存储效率。实验表明，该处理器在CIFAR-10数据集上分类准确率达到87.11%，单张图片推理时间为3.98 ms，能效为183.5 FPS/W，较主流图形处理单元(Graphics Processing Unit,GPU)平台能效提升至2倍以上，与其它SNN处理器相比，推理速度至少提升了4倍，能效至少提升了5倍。With the advancements in hardware-optimized deployment of Spiking Neural Networks (SNNs), SNN processors based on Field-Programmable Gate Arrays (FPGAs) have become a research hotspot due to their efficiency and flexibility. However, existing methods rely on multi-timestep training and reconfigurable computing architectures, which increase computational and memory overhead, reducing deployment efficiency. This work presents a high-efficiency, lightweight residual SNN accelerator that couples algorithmic and hardware co-design to optimize inference energy efficiency. On the algorithm side, we employ single-timesteps training, integrate grouped convolutions, and fuse Batch Normalization (BN) layers, compressing the network to only 0.69 M parameters. Quantization-aware training (QAT) further constrains all weights and activations to 8-bit precision. On the hardware side, intra-layer resource reuse maximizes FPGA utilization, a fully pipelined cross-layer architecture boosts throughput, and on-chip Block RAM (BRAM) stores both network parameters and intermediate results to improve memory efficiency. Experimental results demonstrate that the proposed processor achieves an 87.11% classification accuracy on the CIFAR-10 dataset, with an inference time of 3.98 ms per image and an energy efficiency of 183.5 FPS/W. Compared to mainstream Graphics Processing Unit (GPU) platforms, it achieves over twice the energy efficiency. Furthermore, compared to other SNN processors, it achieves at least a 4×improvement in inference speed and a 5×improvement in energy efficiency.
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[1] Shelhamer E, Long J, Darrell T 2016 IEEE Transactions on Pattern Analysis and Machine Intelligence 39 640
[2] Redmon J, Divvala S, Girshick R, Farhadi A 2016 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, June 26- July 1, 2016 p779
[3] Shi Y, Ou P, Zheng M, Tai H X, Wang Y H, Duan R N, Wu J 2024 Acta Phys. Sin. 73 149（in Chinese） [施岳，欧攀，郑明，邰含旭，王玉红，段若楠，吴坚 2024 物理学报 73 149]
[4] Ying D W, Zhang S H, Deng S J, Wu H B 2023 Acta Phys. Sin. 72 83（in Chinese） [应大卫，张思慧，邓书金，武海斌 2023 物理学报 72 83]
[5] Cao Z Q, Sai B, Lv X 2020 Acta Phys. Sin. 69 41（in Chinese） [曹自强，赛斌，吕欣 2020 物理学报69 41]
[6] Maass W. 1997 Neural Networks 10 1659
[7] Nunes J D, Carvalho M, Carneiro D, Cardoso J S 2022 IEEE Access, 10 60738
[8] Wu C C, Zhou P J, Wang J J, Li G, Hu S G, Yu Q, Liu Y 2022 Acta Phys. Sin. 71 304（in Chinese） [武长春，周莆钧，王俊杰，李国，胡绍刚，于奇，刘洋 物理学报 2022 71 304]
[9] Aliyev I, Svoboda K, Adegbija T, Fellous J M 2024 IEEE 17th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC), Kuala Lumpur, December 16-19, 2024 p413
[10] Merolla P A, Arthur J V, Alvarez-Icaza R, Cassidy A S, Sawada J, Akopyan F, Jackson B L, Imam N, Guo C, Nakamura Y, Brezzo B, Vo I, Esser S K, Appuswamy R, Taba B, Amir A, Flickner M D, Risk W P, Manohar R, Modha D S 2014 Science 345 668
[11] Davies M, Srinivasa N, Lin T H, Chinya G, Cao Y, Choday S H, Dimou G, Joshi P, Imam N, Jain S, Liao Y, Lin C K, Lines A, Liu R, Mathaikutty D, McCoy S, Paul A, Tse J, Venkataramanan G, Weng Y H, Wild A, Yang Y, Wang H 2018 IEEE Micro 38 82
[12] He L, Wang K, Wu C, Tao Z F, Shi X, Miao S Y, Lu S Q 2025 SCIENTIA SINICA Informationis 55 796（in Chinese） [何磊，王堃，吴晨，陶卓夫，时霄，苗斯元，陆少强 中国科学:信息科学 2025 55 796]
[13] Gdaim S, Mtibaa A 2025 Journal of Real-Time Image Processing 22 67.
[14] Yan F, Zheng X W, Meng C, Li C, Liu Y P Modern Electronics Technique 2025 48 151（in Chinese） [严飞，郑绪文，孟川，李楚，刘银萍 现代电子技术 2025 48 151]
[15] Liu Y, Chen Y, Ye W, Gui Y 2022 IEEE Transactions on Circuits and Systems I: Regular Papers 69 2553
[16] Ye W, Chen Y, Liu Y 2022 IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 42 448
[17] Panchapakesan S, Fang Z, Li J 2022 ACM Transactions on Reconfigurable Technology and Systems 15 1
[18] Chen Q, Gao C, Fu Y 2022 IEEE Transactions on Very Large Scale Integration (VLSI) Systems 30 1425
[19] Wang S Q, Wang L, Deng Y, Yang Z J, Guo S S, Kang Z Y, Guo Y F, Xu W X 2020 Journal of Computer Science and Technology 35 475
[20] Biswal M R, Delwar T S, Siddique A, Behera P, Choi Y, Ryu J Y 2022 Sensors 22 8694
[21] Gerlinghoff D, Wang Z, Gu X, Goh R S M, Luo T 2021 IEEE Transactions on Parallel and Distributed Systems 33 3207
[22] Chen Y, Liu Y, Ye W, Chang C C 2023 IEEE Transactions on Circuits and Systems II: Express Briefs 70 3634
[23] Chen Y, Ye W, Liu Y, Zhou H 2024 IEEE Transactions on Circuits and Systems I: Regular Papers 71 6482
[24] Aliyev I, Lopez J, Adegbija T 2024 arXiv:2411.15409[CS-Ar]
[25] Stein R B, Hodgkin A L1967 Proceedings of the Royal Society of London. Series B. Biological Sciences 167 64
[26] Eshraghian J K, Ward M, Neftci E O, Wang X, Lenz G, Dwivedi G 2023 Proceedings of the IEEE 111 1016
[27] Liu H, Chai H F, Sun Q, Yun X, Li X 2023 Engineering 25 61（in Chinese）[刘浩，柴洪峰，孙权，云昕，李鑫 2023 中国工程科学 25 61]
[28] Krizhevsky A, Sutskever I, Hinton G E 2012 Advances in Neural Information Processing Systems 25
[29] Huang G, Liu S, Van der Maaten L, Q. Weinberger K 2018 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, June 18-22, 2018 p2752
[30] Krizhevsky A, Hinton G https://www.cs.toronto.edu/~kriz/cifar.html [2025-3-22]
[31] Ioffe S, Szegedy C 2015 arXiv:1502.03167[CS-LG]
[32] Zheng J W 2021 M.S. Dissertation （Xi’an: Xidian University）(in Chinese)[郑俊伟 2021 硕士学位论文 (西安：西安电子科技大学)]
[33] Jacob B, Kligys S, Chen B, Tang M, Howard A, Adam H, Kalenichenko D 2018 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Sale Lake City, June 18-22, 2018 p2704
[34] Zhou S, Wu Y, Ni Z, Zhou X, Wen H, Zou Y 2016 arXiv:1606.06160[cs.NE]
[35] Liu Z, Cheng K T, Huang D, Xing E P, Shen Z 2022 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, June 19-24, 2022 p4942
[36] Chen Y H, Krishna T, Emer J S, Sze V 2016 IEEE Journal of Solid-State Circuits 52 127
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