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In this paper, we propose Zero Aware Configurable Data Encoding by SkippingTransfer (ZAC-DEST), a data encoding scheme to reduce the energy consumption ofDRAM channels, specifically targeted towards approximate computing and errorresilient applications. ZAC-DEST exploits the similarity between recent datatransfers across channels and information about the error resilience behaviorof applications to reduce on-die termination and switching energy by reducingthe number of 1's transmitted over the channels. ZAC-DEST also provides anumber of knobs for trading off the application's accuracy for energy savings,and vice versa, and can be applied to both training and inference.  We apply ZAC-DEST to five machine learning applications. On average, acrossall applications and configurations, we observed a reduction of $40$% intermination energy and $37$% in switching energy as compared to the state ofthe art data encoding technique BD-Coder with an average output quality loss of$10$%. We show that if both training and testing are done assuming the presenceof ZAC-DEST, the output quality of the applications can be improved upto 9times as compared to when ZAC-DEST is only applied during testing leading toenergy savings during training and inference with increased output quality.

Zero Aware Configurable Data Encoding by Skipping Transfer for Error Resilient Applications