A perspective on physical reservoir computing with nanomagnetic devices

Abstract

Neural networks have revolutionized the area of artificial intelligence and introduced transformative applications to almost every scientific field and industry. However, this success comes at a great price; the energy requirements for training advanced models are unsustainable. One promising way to address this pressing issue is by developing low-energy neuromorphic hardware that directly supports the algorithm's requirements. The intrinsic non-volatility, non-linearity, and memory of spintronic devices make them appealing candidates for neuromorphic devices. Here we focus on the reservoir computing paradigm, a recurrent network with a simple training algorithm suitable for computation with spintronic devices since they can provide the properties of non-linearity and memory. We review technologies and methods for developing neuromorphic spintronic devices and conclude with critical open issues to address before such devices become widely used.

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Cited by in corpus (11)

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• Reservoir Computing and Photoelectrochemical Sensors: A Marriage of Convenience
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• Spintronic Physical Reservoir for Autonomous Prediction and Long-Term Household Energy Load Forecasting
• Exploring Non-Steady-State Charge Transport Dynamics in Information Processing: Insights from Reservoir Computing
• Relaxation pathways and emergence of domains in square artificial spin ice
• Experimental demonstration of enhanced quantum tomography via quantum reservoir processing
• Metrics for spin-based computing
• Predicting the future with magnons
• Reservoir computing by thin film embedded with magnetic impurities