Neuromorphic computing using non-volatile memory

• 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

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...