Author :
Bornholt, James ; Mytkowicz, Todd ; McKinley, Kathryn S.
Abstract :
Building correct, efficient systems that reason about the approximations produced by sensors, machine learning, big data, humans, and approximate hardware and software requires new standards and abstractions. The Uncertain <;T>; software abstraction aims to tackle these pervasive correctness, optimization, and programmability problems and guide hardware and software designers in producing estimates.
Keywords :
Big Data; hardware-software codesign; learning (artificial intelligence); big data; machine learning; programmability problems; sensors; software abstraction; uncertain hardware; uncertain software; Bayes methods; Energy efficiency; Global Positioning System; Probabilistic programming; Programming; Sensors; Uncertainty; approximation; energy efficiency; estimates; probabilistic programming; programming models; uncertainty;
