Associative memories using complex-valued Hopfield networks based on spin-torque oscillator arrays

Prasad, Nitin; Mukim, Prashansa; Madhavan, Advait; Stiles, Mark D.

doi:10.1088/2634-4386/ac7d05

Title: Associative memories using complex-valued Hopfield networks based on spin-torque oscillator arrays

Journal Article · 14 July 2022 · Neuromorphic Computing and Engineering

DOI: https://doi.org/10.1088/2634-4386/ac7d05 · OSTI ID:1875975

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Abstract Simulations of complex-valued Hopfield networks based on spin-torque oscillators can recover phase-encoded images. Sequences of memristor-augmented inverters provide tunable delay elements that implement complex weights by phase shifting the oscillatory output of the oscillators. Pseudo-inverse training suffices to store at least 12 images in a set of 192 oscillators, representing 16 × 12 pixel images. The energy required to recover an image depends on the desired error level. For the oscillators and circuitry considered here, 5% root mean square deviations from the ideal image require approximately 5 μ s and consume roughly 130 nJ. Simulations show that the network functions well when the resonant frequency of the oscillators can be tuned to have a fractional spread less than 10 ⁻³ , depending on the strength of the feedback.

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Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: NONE; SC0019273

OSTI ID:: 1875975

Journal Information:: Neuromorphic Computing and Engineering, Journal Name: Neuromorphic Computing and Engineering Journal Issue: 3 Vol. 2; ISSN 2634-4386

Publisher:: IOP PublishingCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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