Enabling RRAM-Based Brain-Inspired Computation by Co-design of Device, Circuit, and System
Chunmeng Dou, Xiaoxin Xu, Xumeng Zhang, Linfang Wang, Wang Ye, Junjie An, Yang Jianguo, Qing Luo, Tuo Shi, Jing Liu, Dashan Shang, Feng Zhang, Qi Liu, Ming Liu
Abstract
In this work, we discussed the critical challenges, key enabling techniques, and future trends on developing RRAM-based brain-inspired computation, including computing-in-memory (CIM) and neuromorphic computing (NC), from device, circuit to system. To suppress the device non-idealities in the synaptic array, we proposed using optimized bit-cell design and computing approach to reduce the errors and power of the analogue multiply-and-accumulate (MAC). To lower the neuron power, we proposed the sparsity-aware analog-to-digital converter (ADC) for artificial neural networks (ANNs) and highlighted the energy- and area-efficient bio-plausible neurons based on NbO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</inf> devices for spiking neural networks (SNNs). On this basis, we introduce several RRAM CIM designs, followed by a discussion on the remaining challenges and future trends.