Thermal-aware Optimizations of ReRAM-based Neuromorphic Computing Systems

Abstract

ReRAM-based systems are attractive implementation alternatives for neuromorphic computing because of their high speed and low design cost. In this work, we investigate the impact of temperature on the ReRAM-based neuromorphic architectures and show how varying temperatures have a negative impact on the computation accuracy. We first classify ReRAM crossbar cells based on their temperature and identify effective neural network weights that have large impacts on network outputs. Then, we propose a novel temperature-aware training and mapping scheme to prevent the effective weights from being mapped to hot cells to restore the system accuracy. Evaluation results for a two-layer neural network show that our scheme can improve the system accuracy by up to 39.2%.