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1 August 2021 Physics for neuromorphic computing
Danijela Markovic, Alice Mizrahi, Damien Querlioz, Julie Grollier
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Proceedings Volume 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021; 1180408 (2021) https://doi.org/10.1117/12.2591731
Event: SPIE Nanoscience + Engineering, 2021, San Diego, California, United States
Abstract
Neuromorphic computing takes inspiration from the brain to create energy-efficient hardware for information processing, capable of highly sophisticated tasks. Systems built with standard electronics achieve gains in speed and energy by mimicking the distributed topology of the brain. Scaling-up such systems and improving their energy usage, speed and performance by several orders of magnitude requires a revolution in hardware. We discuss how including more physics in the algorithms and nanoscale materials used for data processing could have a major impact in the field of neuromorphic computing. We review striking results that leverage physics to enhance the computing capabilities of artificial neural networks, using resistive switching materials, photonics, spintronics and other technologies. We discuss the paths that could lead these approaches to maturity, towards low-power, miniaturized chips that could infer and learn in real time.
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Danijela Markovic, Alice Mizrahi, Damien Querlioz, and Julie Grollier "Physics for neuromorphic computing", Proc. SPIE 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021, 1180408 (1 August 2021); https://doi.org/10.1117/12.2591731
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KEYWORDS
Physics
Evolutionary algorithms
Algorithm development
Brain
Complex systems
Data processing
Electronics
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