Cryogenic Operation of Computing-In-Memory based Spiking Neural Network

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Abstract
This paper introduces a Computing-In-Memory based Spiking Neural Network (SNN) architecture for cryogenic operation of CMOS (Cryo-SNN). The paper demonstrates design strategies to improve energy efficiency of Cryo-SNN by coupling low-voltage operation at cryogenic temperature with innovative design of neuron circuits optimized for cryogenic conditions. By exploiting the enhanced device characteristics of 14 nm FinFET transistors at cryogenic temperatures, our architecture outlines critical adaptations to SNN components for optimal functionality in extreme environments. The circuit simulation using measurement calibrated 14nm FinFET models shows that a Cryo-SNN designed for MNIST classification operates with 4.54X improved energy-delay-product (EDP) over room temperature operation while maintaining similar accuracy. Further, the paper designs an optimized SNN architecture for autonomous health monitoring of miniaturized satellites at cryogenic temperature consuming less than 1mW of power.
Funding Information
  • DARPA and SRC JUMP2.0 CogniSense

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