Zero Aware Configurable Data Encoding by Skipping Transfer for Error Resilient Applications
Chandan Kumar Jha, Shreyas Singh, Riddhi Thakker, Manu Awasthi and, Joycee Mekie

TL;DR
This paper introduces ZAC-DEST, a data encoding scheme that reduces energy consumption in DRAM channels for error resilient applications by skipping data transfer, with adjustable accuracy-energy trade-offs and significant energy savings demonstrated on machine learning tasks.
Contribution
ZAC-DEST is a novel configurable data encoding method that exploits data similarity and application error resilience to reduce energy use in DRAM channels, applicable during training and inference.
Findings
40% reduction in termination energy
37% reduction in switching energy
up to 9 times improvement in output quality when training and testing with ZAC-DEST
Abstract
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 % in termination energy and % in switching energy as compared…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
