Asymmetric quantization in hardware accelerator

This paper presents an efficient implementation of asymmetric quantization in hardware accelerator for deep learning applications. In this work, we show that asymmetric quantization provides better accuracy performance in AI inferencing with the same amount of storage and bandwidth requirements of a symmetric approach. Also, we provide the method to support the asymmetric approach in digital circuit. The results show that this software and hardware collaboration provide sufficient AI performance while achieving over significant silicon resources reduction.