Energy-Efficient Non-Boolean Computing With Spin Neurons and Resistive Memory

Emerging nonvolatile resistive memory technologies can be potentially suitable for computationally expensive analog pattern-matching tasks. However, the use of CMOS analog circuits with resistive crossbar memory (RCM) would result in large power consumption and poor scalability, thereby eschewing the benefits of RCM-based computation. We explore the potential of emerging spin-torque devices for RCM-based approximate computing circuits. Emerging spin-torque switching techniques may lead to nanoscale, current-mode spintronic switches that can be used for energy-efficient analog-mode data processing. We propose the use of such low-voltage, fast-switching, magnetometallic "spin neurons" for ultralow power non-Boolean computing with RCM. We present the design of analog associative memory for face recognition using RCM, where, substituting conventional analog circuits with spin neurons can achieve ~100× lower power consumption.