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Summary: EnerJ: Approximate Data Types for Safe
and General Low-Power Computation
Adrian Sampson Werner Dietl Emily Fortuna Danushen Gnanapragasam
Luis Ceze Dan Grossman
University of Washington, Department of Computer Science & Engineering
http://sampa.cs.washington.edu/
Abstract
Energy is increasingly a first-order concern in computer systems.
Exploiting energy-accuracy trade-offs is an attractive choice in
applications that can tolerate inaccuracies. Recent work has explored
exposing this trade-off in programming models. A key challenge,
though, is how to isolate parts of the program that must be precise
from those that can be approximated so that a program functions
correctly even as quality of service degrades.
We propose using type qualifiers to declare data that may be
subject to approximate computation. Using these types, the system
automatically maps approximate variables to low-power storage,
uses low-power operations, and even applies more energy-efficient
algorithms provided by the programmer. In addition, the system
can statically guarantee isolation of the precise program component
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