2-Bit-per-Cell RRAM based In-Memory Computing for Area-/Energy-Efficient Deep Learning

Abstract

In-memory computing (IMC) has emerged as a promising technique for enhancing energy-efficiency of deep neural networks (DNN). While embedded non-volatile memory such as resistive RAM (RRAM) is a good alternative to SRAM/ DRAM for IMC owing to high density, low leakage, and non-destructive read, most prior works have not demonstrated using multi-level RRAM devices for array-level IMC operations. In this work, we present an IMC prototype with 2-bit-per-cell RRAM devices for area-/energy-efficient DNN inference. Optimizations on four-level conductance distribution and peripheral circuits with input-splitting scheme have been performed, enabling high DNN accuracy and low area/energy consumption. The prototype chip that monolithically integrated 90nm CMOS and 2-bit-per-cell RRAM array achieves 87% (83%) CIFAR-10 accuracy and 25 (51) TOPS/W energy-efficiency at 1.2 V (0.9 V) supply. At 1.2V, a stable accuracy of 87% is maintained throughout 108 hours. IEEE