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

TL;DR

This paper explores the potential of spin-torque devices as energy-efficient artificial neurons for neuromorphic computing, demonstrating significant energy savings over traditional CMOS-based neurons.

Contribution

It introduces spin-torque devices as a novel hardware approach for bio-inspired computing, showing their advantages in energy efficiency and speed compared to CMOS neuron models.

Findings

Spin neurons mimic neural operations with high energy efficiency.

Spin neurons achieve over 100x lower energy consumption than CMOS neurons.

Significant reduction in energy-delay product for spin neuron implementations.

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.