Ultra low power associative computing with spin neurons and resistive crossbar memory

Authors:
Mrigank Sharad;Deliang Fan;Kaushik Roy
Affiliations:
Purdue University, West Lafayette, IN;Purdue University, West Lafayette, IN;Purdue University, West Lafayette, IN
Venue:
Proceedings of the 50th Annual Design Automation Conference
Year:
2013

Citing 6
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Abstract

Emerging resistive-crossbar memory (RCM) technology can be promising for computationally-expensive analog pattern-matching tasks. However, the use of CMOS analog-circuits with RCM would result in large power-consumption and poor scalability, thereby eschewing the benefits of RCM-based computation. We propose the use of low-voltage, fast-switching, magneto-metallic 'spin-neurons' for ultra low-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 ~100x lower power. This makes the proposed design ~1000x more energy-efficient than a 45nm-CMOS digital ASIC, thereby significantly enhancing the prospects of RCM based computational hardware.