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Neuromorphic computing using non-volatile memory SCOPUS

Title
Neuromorphic computing using non-volatile memory
Authors
Burr, Geoffrey W.Shelby, Robert M.Sebastian, AbuKim, SangbumKIM, SEYOUNGSidler, SeverinVirwani, KumarIshii, MasatoshiNarayanan, PritishFumarola, AlessandroSanches, Lucas L.Boybat, IremLe Gallo, ManuelMOON, KIBONGWoo, JiyooHWANG, HYUNSANGLeblebici, Yusuf
Date Issued
2017-01
Publisher
TAYLOR & FRANCIS LTD
Abstract
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.
URI
https://oasis.postech.ac.kr/handle/2014.oak/103724
DOI
10.1080/23746149.2016.1259585
ISSN
2374-6149
Article Type
Article
Citation
Advances in Physics-x, vol. 2, no. 1, page. 89 - 124, 2017-01
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황현상HWANG, HYUNSANG
Dept of Materials Science & Enginrg
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