Highly efficient neuromorphic computing systems with emerging nonvolatile memories

Brady Taylor; Ziru Li; Bonan Yan; Hai Li; Yiran Chen

doi:10.1117/12.2554915

23 March 2020 Highly efficient neuromorphic computing systems with emerging nonvolatile memories

Brady Taylor, Ziru Li, Bonan Yan, Hai Li, Yiran Chen

Proceedings Volume 11324, Novel Patterning Technologies for Semiconductors, MEMS/NEMS and MOEMS 2020; 113240V (2020) https://doi.org/10.1117/12.2554915
Event: SPIE Advanced Lithography, 2020, San Jose, California, United States

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.

Conference Presentation

Citation Download Citation

Brady Taylor, Ziru Li, Bonan Yan, Hai Li, and Yiran Chen "Highly efficient neuromorphic computing systems with emerging nonvolatile memories", Proc. SPIE 11324, Novel Patterning Technologies for Semiconductors, MEMS/NEMS and MOEMS 2020, 113240V (23 March 2020); https://doi.org/10.1117/12.2554915

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