Neuromorphic computing using non-volatile memory

Dense crossbar arrays of non-volatile memory (NVM) devices represent one possible path for implementing massively-parallel and highly energy-efficient neuromorphic computing systems. We first review recent advances in the application of NVM devices to three computing paradigms: spiking neural networ...

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Bibliographic Details
Published in:	Advances in physics: X Vol. 2; no. 1; pp. 89 - 124
Main Authors:	Burr, Geoffrey W., Shelby, Robert M., Sebastian, Abu, Kim, Sangbum, Kim, Seyoung, Sidler, Severin, Virwani, Kumar, Ishii, Masatoshi, Narayanan, Pritish, Fumarola, Alessandro, Sanches, Lucas L., Boybat, Irem, Le Gallo, Manuel, Moon, Kibong, Woo, Jiyoo, Hwang, Hyunsang, Leblebici, Yusuf
Format:	Journal Article
Language:	English
Published:	Abingdon Taylor & Francis 01-01-2017 Taylor & Francis Ltd Taylor & Francis Group
Subjects:	Algorithms Arrays Artificial neural networks Back propagation Computation Linearity Machine learning Mathematical analysis Matrix algebra Matrix methods Memory devices Multiplication Neural networks Neuromorphic computing non-volatile memory NVM-based neurons NVM-based synapses Power management Random access memory Resistance spike-timing-dependent-plasticity spiking neural networks vector-matrix multiplication
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Summary:	Dense crossbar arrays of non-volatile memory (NVM) devices represent one possible path for implementing massively-parallel and highly energy-efficient neuromorphic computing systems. We first review recent advances in the application of NVM devices to three computing paradigms: spiking neural networks (SNNs), deep neural networks (DNNs), and 'Memcomputing'. In SNNs, NVM synaptic connections are updated by a local learning rule such as spike-timing-dependent-plasticity, a computational approach directly inspired by biology. For DNNs, NVM arrays can represent matrices of synaptic weights, implementing the matrix-vector multiplication needed for algorithms such as backpropagation in an analog yet massively-parallel fashion. This approach could provide significant improvements in power and speed compared to GPU-based DNN training, for applications of commercial significance. We then survey recent research in which different types of NVM devices - including phase change memory, conductive-bridging RAM, filamentary and non-filamentary RRAM, and other NVMs - have been proposed, either as a synapse or as a neuron, for use within a neuromorphic computing application. The relevant virtues and limitations of these devices are assessed, in terms of properties such as conductance dynamic range, (non)linearity and (a)symmetry of conductance response, retention, endurance, required switching power, and device variability.
ISSN:	2374-6149 2374-6149
DOI:	10.1080/23746149.2016.1259585