High performance reconfigurable computing for numerical simulation and deep learning

Lin Gan, Ming Yuan, Jinzhe Yang, Wenlai Zhao, Wayne Luk, Guangwen Yang

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.