Approximate computing is an emerging paradigm enabling tradeoffs between accuracy and efficiency. However, a fundamental challenge persists: state-of-the-art techniques lack the ability to enforce runtime guarantees on accuracy. The convention is to 1) employ training/rollback mechanisms which add complexity, or 2) present experimental results that demonstrate low error, though only empirically/statistically. We offer a solution that revisits concepts from anytime algorithms.
Originally explored for real-time decision problems, anytime algorithms have the property of producing outputs with increasing accuracy over time. We propose the Anytime Automaton, a pipelined computation model that enables anytime approximations via sampling. The automaton executes applications such that 1) they can be interrupted while still delivering valid output, and 2) their accuracy increases over time and is guaranteed to eventually reach precise output. The automaton can be stopped at any point that the user is satisfied, expending just enough time and energy for an acceptable output. If the output is not acceptable, it is a simple matter of letting the application run longer.
By: Joshua San Miguel, Ravi Nair, Vijayalakshmi Srinivasan, Daniel A. Prener
Published in: RC25600 in 2016
Read the article, here: rc25600.pdf