Design of Hopfield network for cryptographic application by spintronic memristors

The exceptional capabilities of the human brain provide inspiration for artificially intelligent hardware that mimics both the function and the structure of neurobiology. In particular, the recent development of nanodevices with biomimetic characteristics promises to enable the development of neuromorphic architectures with exceptional computational efficiency. In this work, we propose biomimetic neurons comprised of domain wall-magnetic tunnel junctions that can be integrated into the first trainable CMOS-free recurrent neural network with biomimetic components. This paper demonstrates the computational effectiveness of this system for benchmark tasks and its superior computational efficiency relative to alternative approaches for recurrent neural networks.

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High-speed CMOS-free purely spintronic asynchronous recurrent neural network

Mathews

Thayil

et al. 2023

APL Machine Learning

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Design of Hopfield network for cryptographic application by spintronic memristors

Cited by 1 publication

References 30 publications

High-speed CMOS-free purely spintronic asynchronous recurrent neural network

High-speed CMOS-free purely spintronic asynchronous recurrent neural network

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