(Invited) Computing with Memristive Devices and Arrays
Authors: Yang J. Joshua 
Published: February 2020

Memristive devices1have become a promising candidate for unconventional computing2due to their attractive properties3. The computing can be implemented on a Resistive Neural Network (ResNN) with memristor synapses and neurons or a Capacitive Neural Network (CapNN) with memcapacitor synapses and neurons.
For ResNNs as computing accelerators, we have built a dot-product engine based on a 128 x 64 1T1R crossbar array using traditional non-volatile memristors with 64 stable analog resistance levels4. Accurate image compression and filtering have been demonstrated with such analog computing accelerator4. In addition, we have demonstrated efficient and self-adaptive in-situlearning in a two-layer neural networks using such memristive arrays5, which is expected to significantly improve the speed and energy efficiency of machine learning.
For ResNNs beyond accelerator applications, we developed diffusive memristors6with diffusion dynamics that is critical for neuromorphic functions. Based on the diffusive memristors, we have further developed artificial synapses7and neurons8to more faithfully emulate their bio-counterparts. We then integrated these artificial synapses and neurons into a small neural network, with which pattern classification and unsupervised learning have been demonstrated8.
For CapNNs, we have developed pseudo-memcapacitive devices based on the diffusive memristors. Capacitive synapses and neurons enabled by these memcapacitive devices have been developed and used to form a fully integrated CapNN9, which has shown spiking signal classification and Hebbian-like learning functions.
Moreover, the memristors can be used for cybersecurity10,11, LSTM12and robotics applications13.
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