InfoX: An Energy-Efficient ReRAM Accelerator Design with Information-Lossless Low-Bit ADCs

Abstract

ReRAM-based accelerators have shown great potential in neural network acceleration via in-memory analog computing. However, high-precision analog-to-digital converters (ADCs), which are required by the ReRAM crossbars to achieve high-accuracy network model inference, play an essential role in the energy-efficiency of the accelerators. Based on the discovery that the ADC precision requirements of crossbars are different, we propose the model-aware crossbarwise ADC precision assignment and the accompanied information-lossless low-bit ADCs to reduce energy overhead without sacrificing model accuracy. In experiments, the proposed information-lossless ReRAM accelerator, InfoX, only consumes 8.97% ADC energy of the SOTA baseline with no accuracy degradation at all.