Exploring Heterogeneous Algorithms for Accelerating Deep Convolutional Neural Networks on FPGAs

Author:

Xiao Qingcheng1,Liang Yun1,Lu Liqiang1,Yan Shengen2,Tai Yu-Wing3

Affiliation:

1. Center for Energy-efficient Computing and Applications, EECS, Peking University

2. Department of Information Engineering, Chinese University of Hong Kong and SenseTime Group Limited

3. SenseTime Group Limited

Publisher

ACM

Cited by 142 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Fusing Depthwise and Pointwise Convolutions for Efficient Inference on GPUs;The 53rd International Conference on Parallel Processing Workshops;2024-08-12

2. A High-Performance Accelerator for Real-Time Super-Resolution on Edge FPGAs;ACM Transactions on Design Automation of Electronic Systems;2024-05-03

3. Pflow: An end-to-end heterogeneous acceleration framework for CNN inference on FPGAs;Journal of Systems Architecture;2024-05

4. WinTA: An Efficient Reconfigurable CNN Training Accelerator With Decomposition Winograd;IEEE Transactions on Circuits and Systems I: Regular Papers;2024-02

5. SSiMD: Supporting Six Signed Multiplications in a DSP Block for Low-Precision CNN on FPGAs;2023 International Conference on Field Programmable Technology (ICFPT);2023-12-12

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