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Proceedings Paper

Highly efficient neuromorphic computing systems with emerging nonvolatile memories
Author(s): Brady Taylor; Ziru Li; Bonan Yan; Hai Li; Yiran Chen
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Paper Abstract

Increased interest in artificial intelligence coupled with a surge in nonvolatile memory research and the inevitable hitting of the "memory wall" in von Neuman computing1 has set the stage for a new flavor of computing systems to flourish: neuromorphic computing systems. These systems are modelled after the brain in hopes of achieving a comparable level of efficiency in terms of speed, power, performance, and size. As it becomes more apparent that digital implementations of neuromorphic systems are far from approaching the brain's level of efficiency, we look to nonvolatile memories for answers. In this paper, we will build up highly-efficient neuromorphic systems by first describing the nonvolatile memory technologies that make them work, exploring methodologies for overcoming statistical device faults, and examining several successful neuromorphic architectures.

Paper Details

Date Published: 23 March 2020
PDF: 12 pages
Proc. SPIE 11324, Novel Patterning Technologies for Semiconductors, MEMS/NEMS and MOEMS 2020, 113240V (23 March 2020); doi: 10.1117/12.2554915
Show Author Affiliations
Brady Taylor, Duke Univ. (United States)
Ziru Li, Duke Univ. (United States)
Bonan Yan, Duke Univ. (United States)
Hai Li, Duke Univ. (United States)
Yiran Chen, Duke Univ. (United States)

Published in SPIE Proceedings Vol. 11324:
Novel Patterning Technologies for Semiconductors, MEMS/NEMS and MOEMS 2020
Martha I. Sanchez; Eric M. Panning, Editor(s)

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