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4 October 2022 Frustrated arrays of nanomagnets for efficient reservoir computing (Conference Presentation)
Alexander Edwards, Dhritiman Bhattacharya, Peng Zhou, Nathan R. McDonald, Lisa Loomis, Clare D. Thiem, Jayasimha Atulasimha, Joseph S. Friedman
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Proceedings Volume PC12205, Spintronics XV; PC122050N (2022) https://doi.org/10.1117/12.2633534
Event: SPIE Nanoscience + Engineering, 2022, San Diego, California, United States
Abstract
We present initial experimental results and simulate a nanomagnet reservoir computer (NMRC) solving tasks requiring high memory content, with an area-energy-delay product ten-million times lower than CMOS systems. We manufactured a small nanomagnet reservoir demonstrating a frustrated state. We evaluated the performance on two novel tasks. Our results indicate the reservoir’s short-term memory capabilities and ability to integrate information from multiple concurrent inputs. In the end, our system saw a reduction in area by a factor of 50,000, in energy by a factor of 60, and in period by a factor of four as compared with an equivalent CMOS reservoir.
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Alexander Edwards, Dhritiman Bhattacharya, Peng Zhou, Nathan R. McDonald, Lisa Loomis, Clare D. Thiem, Jayasimha Atulasimha, and Joseph S. Friedman "Frustrated arrays of nanomagnets for efficient reservoir computing (Conference Presentation)", Proc. SPIE PC12205, Spintronics XV, PC122050N (4 October 2022); https://doi.org/10.1117/12.2633534
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KEYWORDS
Manufacturing
Computer simulations
Electron beam lithography
Machine learning
Magnetism
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