Probability type inference for flexible approximate programming

8 years 12 days ago

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In approximate computing, programs gain efﬁciency by allowing occasional errors. Controlling the probabilistic effects of this approximation remains a key challenge. We propose a new approach where programmers use a type system to communicate high-level constraints on the degree of approximation. A combination of type inference, code specialization, and optional dynamic tracking makes the system expressive and convenient. The core type system captures the probability that each operation exhibits an error and bounds the probability that each expression deviates from its correct value. Solver-aided type inference lets the programmer specify the correctness probability on only some variables—program outputs, for example—and automatically ﬁlls in other types to meet these speciﬁcations. An optional dynamic type helps cope with complex run-time behavior where static approaches are insufﬁcient. Together, these features interact to yield a high degree of programmer control while ...

Brett Boston, Adrian Sampson, Dan Grossman, Luis C

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