Comparative study of DNN accelerators on FPGA

Yixuan Zhao; Feiyang Liu; Tanbao Yan; Han Gao

doi:10.1117/12.2671578

29 March 2023 Comparative study of DNN accelerators on FPGA

Yixuan Zhao, Feiyang Liu, Tanbao Yan, Han Gao

Proceedings Volume 12594, Second International Conference on Electronic Information Engineering and Computer Communication (EIECC 2022); 125940G (2023) https://doi.org/10.1117/12.2671578
Event: Second International Conference on Electronic Information Engineering and Computer Communication (EIECC 2022), 2022, Xi'an, China

Abstract

As an important technology and research direction to achieve AI, deep learning has been widely applied in the fields of computer vision, speech recognition, and natural language processing. How to effectively accelerate the computing power of deep learning has always been the focus of scientific research. Among various acceleration technologies, FPGA has the advantages of reconfiguration, high performance, small size, and low latency. As more and more FPGA-based neural network accelerators are developed, we notice there is a lack of a complete and detailed overview. In this paper, we give a comparative study of DNN accelerators on FPGA from the aspects of hardware structures, design ideas and optimization strategies. We further compare the performance of different acceleration technologies in different models and present the prospects of the FPGA accelerators for deep learning.

Citation Download Citation

Yixuan Zhao, Feiyang Liu, Tanbao Yan, and Han Gao "Comparative study of DNN accelerators on FPGA", Proc. SPIE 12594, Second International Conference on Electronic Information Engineering and Computer Communication (EIECC 2022), 125940G (29 March 2023); https://doi.org/10.1117/12.2671578

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