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High performance reconfigurable computing for numerical simulation and deep learning

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Abstract

Due to their customizable on-chip resources, reconfigurable computing platforms such as FPGAs are able to achieve better time-to-solution and energy-to-solution than general-purpose processors. They have been widely adopted in many important applications, from traditional numerical processing to emerging deep learning systems. Since FPGAs have become promising options for current and future high performance computing, this report summarises and analyses recent FPGA-related efforts, including the latest industrial approaches, the state-of-the-art reconfigurable solutions, and various issues such as on-chip resources and development productivity.

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Acknowledgements

This work was supported in part by the National Key R&D Program of China (grant no. 2016YFA0602200, and 2017YFA0604500), the National Natural Science Foundation of China (grant nos. 61672312, 41374113, 91530323, U1839206, 61962051), and by Center for High Performance Computing and System Simulation, Pilot National Laboratory for Marine Science and Technology (Qingdao).

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Authors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Lin Gan, Wenlai Zhao & Guangwen Yang

  2. Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China

    Lin Gan, Wenlai Zhao & Guangwen Yang

  3. National Supercomputing Center in Wuxi, Jiangsu, China

    Lin Gan, Ming Yuan, Jinzhe Yang, Wenlai Zhao & Guangwen Yang

  4. School of Internet of Things Engineering, Jiangnan University, Jiangsu, China

    Ming Yuan

  5. Department of Computing, Imperial College London, London, UK

    Jinzhe Yang & Wayne Luk

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  1. Lin Gan
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Gan, L., Yuan, M., Yang, J. et al. High performance reconfigurable computing for numerical simulation and deep learning. CCF Trans. HPC 2, 196–208 (2020). https://doi.org/10.1007/s42514-020-00032-x

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