Stochastic single flux quantum neuromorphic computing using magnetically tunable Josephson junctions

Abstract

Single flux quantum (SFQ) circuits form a natural neuromorphic technology with SFQ pulses and superconducting transmission lines simulating action potentials and axons, respectively. Here we present a new component, magnetic Josephson junctions, that have a tunablility and re-configurability that was lacking from previous SFQ neuromorphic circuits. The nanoscale magnetic structure acts as a tunable synaptic constituent that modifies the junction critical current. These circuits can operate near the thermal limit where stochastic firing of the neurons is an essential component of the technology. This technology has the ability to create complex neural systems with greater than 10^21 neural firings per second with approximately 1 W dissipation.

2016 IEEE International Conference on Rebooting Computing (ICRC)

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Cited by in corpus (3)

• Superconducting Neuromorphic Computing Using Quantum Phase-Slip Junctions
• Physical Neural Networks with Self-Learning Capabilities
• A self-learning magnetic Hopfield neural network with intrinsic gradient descent adaption