Deep Neural Network Model and FPGA Accelerator Co-Design: Opportunities and Challenges

Cong Hao, Deming Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With an explosive growth of various neural network algorithms, their high performance implementations on hardware platforms, such as GPUs and FPGAs, are becoming critical as well. Compared to widely used GPUs, FPGAs are considered to be harder for design and optimization even with the help of High Level Synthesis (HLS) tools. However, recent studies have shown that FPGAs can outperform GPUs in speed and power/energy efficiency; both factors are important in machine learning applications. In this paper, we will discuss a simultaneous DNN and hardware accelerator co-design method to push the DNN performance on FPGAs. We first summarize existing techniques and results along this direction, and then propose new ideas to further improve DNN development productivity and design quality. Finally we discuss the challenges we would face and propose some potential solutions.

Original languageEnglish (US)
Title of host publication2018 14th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2018 - Proceedings
EditorsTing-Ao Tang, Fan Ye, Yu-Long Jiang
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538644409
DOIs
StatePublished - Dec 5 2018
Event14th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2018 - Qingdao, China
Duration: Oct 31 2018Nov 3 2018

Publication series

Name2018 14th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2018 - Proceedings

Other

Other14th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2018
CountryChina
CityQingdao
Period10/31/1811/3/18

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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    Hao, C., & Chen, D. (2018). Deep Neural Network Model and FPGA Accelerator Co-Design: Opportunities and Challenges. In T-A. Tang, F. Ye, & Y-L. Jiang (Eds.), 2018 14th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2018 - Proceedings [8564956] (2018 14th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSICT.2018.8564956