The unprecedented progress in computational technologies led to a substantial proliferation
of artificial intelligence applications, notably in the era of big data and IoT devices. In the …

Although approximate computing promises better performance for applications allowing
marginal errors, dearth of hardware support and lack of run-time accuracy guarantees …

Approximate memories reduce power and increase energy efficiency, at the expense of
errors in stored data. These errors may be tolerated, up to a point, by many applications with …

Many application domains including graph analytics, the Internet-of-Things, precision
agriculture, and media processing operate on noisy data and/or produce approximate …

Instruction-level error injection analyses aim to find instructions where errors often lead to
unacceptable outcomes like Silent Data Corruptions (SDCs). These analyses require …

Modern computations must regularly interact with imprecise sensors, deal with hardware
failures, and operate on incomplete or inaccurate input data. Developers may also resort to …

Approximate memories provide energy savings or performance improvements at the cost of
occasional errors in stored data. Applications that tolerate errors on their data profit from this …

To efficiently harden programs susceptible to Silent Data Corruptions (SDCs), developers
need to invoke error injection analyses to find particularly vulnerable instructions and then …

In this work, we introduce the Fluid framework, a set of language, compiler and runtime
extensions that allow for the expression of regions within which dataflow dependencies can …

How should we build applications for large-scale networked embedded systems--now in the
incarnation of the Internet of Things--when we do not want to rely on the existence of a …