Re(2)fresh: A Framework for Mitigating Read Disturbance in ReRAM-based DNN Accelerators

Abstract

A severe read disturbance problem degrades the inference accuracy of a resistive RAM (ReRAM) based deep neural network (DNN) accelerator. Refresh, which reprograms the ReRAM cells, is the most obvious solution for the problem, but programming ReRAM consumes huge energy. To address the issue, we first analyze the resistance drift pattern of each conductance state and the actual read stress applied to the ReRAM array by considering the charac- teristics of ReRAM-based DNN accelerators. Based on the analysis, we cluster ReRAM cells into a few groups for each layer of DNN and generate a proper refresh cycle for each group in the offline phase. The individual refresh cycles reduce energy consumption by reduc- ing the number of unnecessary refresh operations. In the online phase, the refresh controller selectively launches refresh operations according to the generated refresh cycles. ReRAM cells are selec- tively refreshed by minimally modifying the conventional structure of the ReRAM-based DNN accelerator. The proposed work success- fully resolves the read disturbance problem by reducing 97% of the energy consumption for the refresh operation while preserving inference accuracy.