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4 October 2022 Implementation of a binary neural network on a passive array of magnetic tunnel junctions (Conference Presentation)
Jonathan M. Goodwill, Nitin Prasad, Brian D. Hoskins, Matthew W. Daniels, Advait Madhavan, Lei Wan, Tiffany S. Santos, Michael Tran, Jordan A Katine, Patrick M. Braganca, Mark D. Stiles, Jabez McClelland
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Proceedings Volume PC12205, Spintronics XV; PC122050L (2022) https://doi.org/10.1117/12.2632314
Event: SPIE Nanoscience + Engineering, 2022, San Diego, California, United States
Abstract
Magnetic tunnel junctions (MTJs) provide an attractive platform for implementing neural networks because of their simplicity, nonvolatility and scalability. In a hardware realization, however, device variations, write errors, and parasitic resistance will generally degrade performance. To quantify such effects, we perform experiments on a 2-layer perceptron constructed from a 15 × 15 passive array of MTJs, examining classification accuracy and write fidelity. Despite imperfections, we achieve accuracy of up to 95.3 % with proper tuning of network parameters. The success of this tuning process shows that new metrics are needed to characterize and optimize networks reproduced in mixed signal hardware.
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Jonathan M. Goodwill, Nitin Prasad, Brian D. Hoskins, Matthew W. Daniels, Advait Madhavan, Lei Wan, Tiffany S. Santos, Michael Tran, Jordan A Katine, Patrick M. Braganca, Mark D. Stiles, and Jabez McClelland "Implementation of a binary neural network on a passive array of magnetic tunnel junctions (Conference Presentation)", Proc. SPIE PC12205, Spintronics XV, PC122050L (4 October 2022); https://doi.org/10.1117/12.2632314
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KEYWORDS
Neural networks
Binary data
Magnetism
Signal processing
Instrument modeling
Performance modeling
Resistance
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