High-Performance Mixed-Low-Precision CNN Inference Accelerator on FPGA

Low-precision techniques can effectively reduce the computational complexity and bandwidth requirements of a convolutional neural network (CNN) inference, but may lead to significant accuracy degradation. Mixed-low-precision techniques provide a superior approach for CNN inference since it can take the advantages of low precision while maintaining accuracy. In this article, we propose a high-performance, highly flexible {W^8A^8}W8A8 (INT8 weight and INT8 activation) and {W^T A^2}WTA2 (TERNARY weight and INT2 activation) mixed-precision CNN inference hardware architecture, DPUmxp, designed and implemented on Xilinx Virtex UltraScale+13P FPGA with peak performance up to 58.9 TOPS.