Reconfigurable Multifunction Computing Unit Using an Universal Piecewise Linear Method

Abstract

Computing units for nonlinear complex functions are indispensable in deep neural network training processors. However, the existing computing units for nonlinear complex functions have low utilization efficiency and poor agreement and precision. In this article, we propose a multifunction computing unit for training deep neural networks by reusing computing resources based on a piecewise linear (PWL) method to improve computing density. Based on the state-of-the-art segmentor of PWL method, multiple nonlinear functions are divided into the fewest segments with the same bit width of computation. In hardware implementation, the reconfigurable technique is implemented on multiple functions while reusing computing resources including the multiplier and adder. The application-specific integrated circuit (ASIC) implementation results reveal that the architecture with reuse reduces the area by 44.50% and the power by 43.71% at the same frequency, when compared with the architecture without reuse.