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Leveraging Software Testing to Explore Input Dependence for Approximate Computing

With the end of Moore’s law and the shift towards alternative paradigms for energy and performance gains, approximate computing has gained considerable traction in recent years. Approximate computing environments obtain gains in performance and/or energy by trading off computational accuracy. The gains afforded by many approximation techniques are intimately coupled with the inputs provided to the program. Hence, reasoning about inputs in a systematic way is an important and open research problem. In this paper, we discuss some concerns associated with input dependence and the need to elevate program inputs to first-class citizens within the domain of approximate computing. We leverage the mature field of software testing for mechanisms and techniques to analyze program inputs and measure their influence on approximations. We believe that, going forward, software testing methodologies will become an integral part of the workflow for approximate computing-based systems.