Quantized Neural Network Synthesis for Direct Logic Circuit Implementation

Abstract

Hardware acceleration enables neural network (NN) inferencing on edge devices and for high throughput applications. Most approaches use neural processing elements for computation while storing weights in memory blocks. To avoid costly memory access, recent efforts seek direct logic implementation with weights hardwired into the circuit. However, special training strategies are often needed, and they could not maintain accuracy. In contrast, we take a trained and quantized NN as input and synthesize it by Booth encoding and logic sharing, resulting in a hardware accelerator without degrading accuracy. Experiments demonstrate that our method outperforms existing work in area reduction and/or throughput and power efficiency.