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This position paper advocates a communicationsinspired approach to the design of machine learning systems on energy-constrained embedded 'always-on' platforms. The communicationsinspired approach has two versions 1) a deterministic version where existing low-power communication IC design methods are repurposed, and 2) a stochastic version referred to as Shannon-inspired statistical information processing employing information-based metrics, statistical error compensation (SEC), and retraining-based methods to implement ML systems on stochastic circuit/device fabrics operating at the limits of energy-efficiency. The communications-inspired approach has the potential to fully leverage the opportunities afforded by ML algorithms and applications in order to address the challenges inherent in their deployment on energy-constrained platforms.
Output:
Energy-efficient Machine Learning in Silicon: A Communications-inspired Approach