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.2

STT-SNN: A Spin-Transfer-Torque Based Soft-Limiting Non-Linear Neuron for Low-Power Artificial Neural Networks

“…12a. Compared with CMOS analog and digital neurons in [15,27], STT-SNN leads to the possibility of more than two orders of magnitude lower energy dissipation. The LSV-based spin-neuron (step function) is around one order of magnitude larger than STT-SNN because of the large hard-axis preset energy [13].…”

“…It acts as a low power and compact current comparator that can be employed as energy efficient current mode hard limiting step function artificial neuron. Note that, this current can be further reduced by lowering the energy barrier or applying spin-orbital coupling [15].…”

“…Previously, we proposed the application of hard-limiting spin-neurons based on lateral spin valves (LSV) [13], as well as domain wall motion (DWM) magnets [14,15] for designing ultra-low power artificial neural networks. Fig.…”

“…Such magnetometallic devices can operate at ultra-low terminal voltages and can implement current-mode summation and non-linear operations required by an artificial neuron. We previously proposed the application of spin-torque neurons based on lateral spin value (LSV) and domain wall motion (DWM) magnet for designing ultra-low power neural networks [13][14][15]. However, all of the previously proposed spin-neurons implement the hard-limiting step-function, which leads to larger network size, and simply cannot provide adequate modeling accuracy for complex classification problems.…”

Hierarchical Temporal Memory Based on Spin-Neurons and Resistive Memory for Energy-Efficient Brain-Inspired Computing

“…Such magneto-metallic devices can operate at ultra-low terminal voltages and can implement current-mode summation and comparison operations at ultra-low energy cost. Such current-mode spin switches or 'neurons' can be exploited in energy efficient analog-mode computing [28,34,35]. In this work we present the design of RCN based energy-efficient computing blocks for HTM using such 'spin neurons'.…”