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

Author:

Mathews Pranav O.12ORCID,Duffee Christian B.2,Thayil Abel23ORCID,Stovall Ty E.2,Bennett Christopher H.4ORCID,Garcia-Sanchez Felipe5ORCID,Marinella Matthew J.46ORCID,Incorvia Jean Anne C.7,Hassan Naimul2ORCID,Hu Xuan2ORCID,Friedman Joseph S.2ORCID

Affiliation:

1. School of Electrical and Computer Engineering, Georgia Institute of Technology 1 , Atlanta, Georgia 30332, USA

2. Department of Electrical and Computer Engineering, The University of Texas at Dallas 2 , Richardson, Texas 75080, USA

3. Laboratoire de Physique de la Matière Condensée, Ecole Polytechnique, CNRS, IP Paris 3 , Palaiseau 91128, France

4. Sandia National Laboratories 4 , Albuquerque, New Mexico 87185, USA

5. Departamento de Física Aplicada, Universidad de Salamanca 5 , Salamanca 37008, Spain

6. School of Electrical, Computer and Energy Engineering, Arizona State University 6 , Tempe, Arizona 85287, USA

7. Department of Electrical and Computer Engineering, The University of Texas at Austin 7 , Austin, Texas 78712, USA

Abstract

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.

Funder

National Science Foundation

TxACE Analog Center of Excellence

Publisher

AIP Publishing

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Hopfield vs Ising: A Comparison on the SoC FPAA;IEEE Transactions on Circuits and Systems I: Regular Papers;2024-09

2. Stochastic domain wall-magnetic tunnel junction artificial neurons for noise-resilient spiking neural networks;Applied Physics Letters;2023-06-26

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