Neuromorphic Computing with Resistive Memory and Bayesian Machines

Abstract

Bio-inspired computing with memristors and neuristors offers promising pathways to energy-efficient intelligence. This work reviews toolkits for implementing spiking neural networks and Bayesian machine learning directly in hardware using these emerging devices. We first demonstrate that normally passive memristors can exhibit neuristor-like oscillatory behavior when heating and cooling is taken into account. Such oscillations enable spike-based neural computing. We then summarize recent works on leveraging intrinsic switching stochasticity in memristive devices to physically embed Bayesian models and perform in-situ probabilistic inference. While still facing challenges in endurance, variation tolerance, and peripheral circuitry, this co-design approach combining tailored algorithms and nanodevices could enable a new class of ultra-low power brain-inspired intelligence tolerant to uncertainty and capable to learn with small datasets. Longer-term, hybrid CMOS-memristor systems with sensing/actuation may provide fully adaptive Bayesian edge intelligence. Overall, the confluence of probabilistic algorithms and memristive hardware holds promise for future electronics combining efficiency, adaptability, and human-like reasoning. Academic innovations exploring this algorithm-hardware co-design can lay the foundation for this emerging paradigm of probabilistic cognitive computing.