Low Precision Floating Point Arithmetic for High Performance FPGA-based CNN Acceleration

@article{Wu2020LowPF,
  title={Low Precision Floating Point Arithmetic for High Performance FPGA-based CNN Acceleration},
  author={Chen Wu and Mingyu Wang and Xinyuan Chu and Kun Wang and Lei He},
  journal={Proceedings of the 2020 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays},
  year={2020}
}
  • Chen Wu, Mingyu Wang, Lei He
  • Published 23 February 2020
  • Computer Science
  • Proceedings of the 2020 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays
Low precision data representation is important to reduce storage size and memory access for convolutional neural networks (CNNs). Yet, existing methods have two major limitations: (1) requiring re-training to maintain accuracy for deep CNNs, and (2) needing 16-bit floating point or 8-bit fixed point for a good accuracy. In this paper, we propose a low precision (8-bit) floating point (LPFP) quantization method for FPGA-based acceleration to overcome the above limitations. Without any re… 
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3 Citations

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