Approximate computing is an emerging area for trading off the accuracy
\nof an application for improved performance, lower energy costs, and tolerance
\nto unreliable hardware. However, developers must ensure that the leveraged
\napproximations do not introduce significant, intolerable divergence from the reference
\nimplementation, as specified by several established robustness criteria. In
\nthis work, we show the application of automated differential verification towards
\nverifying relative safety, accuracy, and termination criteria for a class of program
\napproximations. We use mutual summaries to express relative specifications for
\napproximations, and SMT-based invariant inference to automate the verification
\nof such specifications. We perform a detailed feasibility study showing promise
\nof applying automated verification to the domain of approximate computing in a
\ncost-effective manner.<\/p>\n","protected":false},"excerpt":{"rendered":"
Approximate computing is an emerging area for trading off the accuracy of an application for improved performance, lower energy costs, and tolerance to unreliable hardware. However, developers must ensure that the leveraged approximations do not introduce significant, intolerable divergence from the reference implementation, as specified by several established robustness criteria. 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