Boolean and Non-Boolean Computation With Spin Devices

TL;DR

This paper explores the use of spin torque devices for non-Boolean computation, demonstrating significant energy efficiency improvements over traditional CMOS in various applications.

Contribution

It introduces spin-torque based neuron models for non-Boolean architectures, showcasing their potential for ultra-low energy analog processing.

Findings

Achieves 15X-100X lower energy consumption than CMOS

Demonstrates applicability in image processing and pattern matching

Shows feasibility of spin devices for non-Boolean computation

Abstract

Recently several device and circuit design techniques have been explored for applying nano-magnets and spin torque devices like spin valves and domain wall magnets in computational hardware. However, most of them have been focused on digital logic, and, their benefits over robust and high performance CMOS remains debatable. Ultra-low voltage, current-switching operation of magneto-metallic spin torque devices can potentially be more suitable for non-Boolean computation schemes that can exploit current-mode analog processing. Device circuit co-design for different classes of non-Boolean-architectures using spin-torque based neuron models in spin-CMOS hybrid circuits show that the spin-based non-Boolean designs can achieve 15X-100X lower computation energy for applications like, image-processing, data-conversion, cognitive-computing, pattern matching and programmable-logic, as compared to state of art CMOS designs.