A Unified Framework for Training, Mapping and Simulation of ReRAM-Based Convolutional Neural Network Acceleration

Abstract

ReRAM-based neural network accelerators (RNAs) could outshine their digital counterparts in terms of computational efficiency and performance remarkably. However, some open software tool for broad architectural exploration and end-to-end evaluation are still missing. We present a simulation framework of RNA for CNN inference that encompasses a ReRAM-aware NN training tool, a CNN-oriented mapper and a micro-architecture simulator. Main characteristics of ReRAM and circuits are reflected by the configurable simulator, as well as by the customized training algorithm. The function of the simulator’s core components is verified by the corresponding circuit simulation of a real chip design. This framework enables comprehensive architectural exploration and end-to-end evaluation, and it’s preliminary version is available at https://github.com/CRAFT-THU/XB-Sim.