  In this paper, we propose Zero Aware Configurable Data Encoding by Skipping
Transfer (ZAC-DEST), a data encoding scheme to reduce the energy consumption of
DRAM channels, specifically targeted towards approximate computing and error
resilient applications. ZAC-DEST exploits the similarity between recent data
transfers across channels and information about the error resilience behavior
of applications to reduce on-die termination and switching energy by reducing
the number of 1's transmitted over the channels. ZAC-DEST also provides a
number 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, across
all applications and configurations, we observed a reduction of $40$% in
termination energy and $37$% in switching energy as compared to the state of
the 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 presence
of ZAC-DEST, the output quality of the applications can be improved upto 9
times as compared to when ZAC-DEST is only applied during testing leading to
energy savings during training and inference with increased output quality.
