Enabling system-level platform resilience through embedded data-driven inference capabilities in electronic devices

Abstract

Advanced devices for embedded and ambient applications represent one of the most compelling classes of electronic systems, but they also impose more severe constraints on system resources than ever before. Although platform non-idealities have always posed a fundamental limitation, the overheads of conventional margining are now reaching intolerable levels. We describe an alternate approach to hardware resilience that applies to applications where advanced modeling and inference capabilities are required, a rapidly increasing emphasis in many applications. We show how a data-driven modeling framework for analyzing application data can also be used to effectively model and overcome a broad range of hardware non-idealities. Specific examples for biomedical sensors are shown that are able to retain performance with minimal on-line overhead despite the presence of severe digital- and analog-circuit non-idealities.