(Invited) Computing with Memristive Devices and Arrays

Abstract

Memristive devices have become a promising candidate for unconventional computing due to their attractive properties(1). 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(2).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 levels(3). With such computation accelerators, we have demonstrated efficient inference and learning with traditional Machine Learning algorithms(4-7), which is expected to significantly improve the speed and energy efficiency of neural networks.For ResNNs beyond accelerator applications, we developed diffusive memristors(8)with diffusion dynamics that is critical for neuromorphic functions. Based on the diffusive memristors, we have further developed artificial synapses(8)and neurons(9)to 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 demonstrated(9).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 CapNN(10), which can implement spiking signal classification and Hebbian-like learning. Z. Wanget al., Resistive switching materials for information processing. Nature Reviews Materials 5, 173–195 (2020). Q. Xia, J. J. Yang, Memristive crossbar arrays for brain-inspired computing. Nature materials 18, 309-323 (2019). C. Liet al., Analogue signal and image processing with large memristor crossbars. Nature Electronics 1, 52-59 (2018). Z. Wanget al., Reinforcement learning with analogue memristor arrays. Nature Electronics 2, 115-124 (2019). Z. Wanget al., In situ training of feed-forward and recurrent convolutional memristor networks. Nature Machine Intelligence 1, 434-442 (2019). C. Liet al., Long short-term memory networks in memristor crossbar arrays. Nature Machine Intelligence 1, 49-57 (2019). P. Linet al., Three-dimensional memristor circuits as complex neural networks. Nature Electronics 3, 225–232 (2020). Z. Wanget al., Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nature Materials 16, 101-108 (2017). Z. Wanget al., Fully memristive neural networks for pattern classification with unsupervised learning. Nature Electronics 1, 137-145 (2018). Z. Wanget al., Capacitive neural network with neuro-transistors. Nature Communications 9, 3208 (2018).