Prof. Keshav Pingali, Department of Computer Science, The University of Texas at Austin.
Title: Proactive Control of Approximate Programs
Approximate computing trades off accuracy of results for resources such as energy, computing time, and memory usage. There is a large and rapidly growing literature on approximate computing that has focused mostly on showing the benefits of approximate computing or on programming constructs for verifying that approximation is confined to certain parts of the program such as computations that do not affect control-flow. However, we know relatively little about how to control approximation in a disciplined way.
In this talk, we address the problem of proactive control of approximation in non-streaming programs that have a set of "knobs" that can be dialed up or down to control the level of approximation of different components in the program. Our approach uses machine learning techniques to learn cost and error models for the program, and uses these models to choose, for a desired level of approximation, knob settings that minimize running time or energy consumption. Experimental results with several complex benchmarks from different problem domains show that this is a promising approach for proactive control of approximate programs.

ANDARE'17 Program

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