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Neural Acceleration For General-Purpose Approximate Programs

This paper describes a new approach that uses machine learning-based transformations to accelerate approximation-tolerant programs. Hadi Esmaeilzadeh, Adrian Sampson, Luis Ceze, Doug Burger
From Communications of the ACM | January 2015

From Communications of the ACM

Two Hardware-Based Approaches For Deterministic Multiprocessor Replay

Modern computer systems are inherently nondeterministic due to a variety of events that occur during an execution. The lack of repeatability that arises from this... Derek R. Hower, Pablo Montesinos, Luis Ceze, Mark D. Hill, Josep Torrellas
From Communications of the ACM | June 2009