Spin Neurons: A Possible Path to Energy-Efficient Neuromorphic Computers

Abstract

Recent years have witnessed growing interest in the field of brain-inspired computing based on neural-network architectures. In order to translate the related algorithmic models into powerful, yet energy-efficient cognitive-computing hardware, computing-devices beyond CMOS may need to be explored. The suitability of such devices to this field of computing would strongly depend upon how closely their physical characteristics match with the essential computing primitives employed in such models. In this work we discuss the rationale of applying emerging spin-torque devices for bio-inspired computing. Recent spin-torque experiments have shown the path to low-current, low-voltage and high-speed magnetization switching in nano-scale magnetic devices. Such magneto-metallic, current-mode spin-torque switches can mimic the analog summing and thresholding operation of an artificial neuron with high energy-efficiency. Comparison with CMOS-based analog circuit-model of neuron shows that spin neurons can achieve more than two orders of magnitude lower energy and beyond three orders of magnitude reduction in energy-delay product. The application of spin neurons can therefore be an attractive option for neuromorphic computers of future.

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Cited by in corpus (14)

• A Survey of Neuromorphic Computing and Neural Networks in Hardware
• Proposal for an All-Spin Artificial Neural Network: Emulating Neural and Synaptic Functionalities Through Domain Wall Motion in Ferromagnets
• Probabilistic Deep Spiking Neural Systems Enabled by Magnetic Tunnel Junction
• Straintronics: Manipulating the Magnetization of Magnetostrictive Nanomagnets with Strain for Energy-Efficient Applications
• STT-SNN: A Spin-Transfer-Torque Based Soft-Limiting Non-Linear Neuron for Low-Power Artificial Neural Networks
• Hierarchical Temporal Memory Based on Spin-Neurons and Resistive Memory for Energy-Efficient Brain-Inspired Computing
• Energy-efficient switching of nanomagnets for computing: Straintronics and other methodologies
• From materials to systems: a multiscale analysis of nanomagnetic switching
• Antiferromagnet-Based Neuromorphics Using Dynamics of Topological Charges
• Analog Signal Processing Using Stochastic Magnets
• Evaluating Spintronic Devices Using The Modular Approach
• Spin-Orbit-Torque-based Devices, Circuits and Architectures
• Comparison of measured and simulated spin-wave mode spectra of magnetic nanostructures
• Hybrid Piezoelectric-Magnetic Neurons: A Proposal for Energy-Efficient Machine Learning