Design of Hopfield Networks Based on Superconducting Coupled Oscillators

Cheng, Ran; Kirst, Christoph; Vasudevan, Dilip

doi:10.1109/tasc.2025.3545409

Design of Hopfield Networks Based on Superconducting Coupled Oscillators

Journal Article · Wed Mar 26 00:00:00 EDT 2025 · IEEE Transactions on Applied Superconductivity

DOI:https://doi.org/10.1109/tasc.2025.3545409· OSTI ID:3000198

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Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Univ. of California, San Francisco, CA (United States)

The global energy shortage has driven the development of many energy-efficient computational platforms beyond Moore's law, among which brain-inspired neuromorphic computing is one of the promising solutions. Associative memory and pattern recognition are important computations solved by brain-inspired Hopfield networks. Classical Hopfield networks store memories via fixed point attractors of their dynamics. In oscillatory Hopfield networks, these attractors are replaced by periodic orbits. Here, in this study, we design an oscillatory Hopfield network based on coupled superconducting oscillators. We first employ a mathematical phase reduction approach to map networks of coupled superconducting rapid single flux quantum (RSFQ) ring oscillators to coupled Kuramoto phase-oscillator networks. We use this theory to numerically optimize the hardware's mutual inductances in order to directly match the phase-reduced superconducting oscillators to a model of phase-oscillator-based Hopfield networks. The resulting network can store multiple oscillatory phase-locked memory patterns and recover the patterns based on the initial phase conditions. As different pattern recognition tasks, or learning, require tunable connectivity strengths between the oscillatory nodes, we further employ a coupler circuit that enables tuning the coupling strength between two oscillators by applying an external flux. We demonstrate the functionality of our design through numerical simulations of a small example network with oscillators operating at 86 GHz and recognizing patterns within 10 ns. Our approach enables the learning and retrieval of dynamical memory patterns with a wide range of applications where rhythmic dynamic output is beneficial.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 3000198

Journal Information:: IEEE Transactions on Applied Superconductivity, Journal Name: IEEE Transactions on Applied Superconductivity Journal Issue: 5 Vol. 35; ISSN 1051-8223; ISSN 2378-7074; ISSN 1558-2515

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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