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This position paper advocates a communications-inspired approach to thedesign of machine learning systems on energy-constrained embedded `always-on'platforms. The communications-inspired approach has two versions - 1) adeterministic version where existing low-power communication IC design methodsare repurposed, and 2) a stochastic version referred to as Shannon-inspiredstatistical information processing employing information-based metrics,statistical error compensation (SEC), and retraining-based methods to implementML systems on stochastic circuit/device fabrics operating at the limits ofenergy-efficiency. The communications-inspired approach has the potential tofully leverage the opportunities afforded by ML algorithms and applications inorder to address the challenges inherent in their deployment onenergy-constrained platforms.

Energy-efficient Machine Learning in Silicon: A Communications-inspired Approach