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General-purpose Code Acceleration with Limited-precision Analog Computation by Renée St. Amant; Amir Yazdanbakhsh; Jongse Park; Bradley Thwaites; Hadi Esmaeilzadeh; Arjang Hassibi; Luis Ceze; Doug Burger is a Computer Science article available to read on EtoBox.

What is General-purpose Code Acceleration with Limited-precision Analog Computation about?

As improvements in per-transistor speed and energy efficiency diminish, radical departures from conventional approaches are becoming critical to improving the performance and energy efficiency of general-purpose processors. We propose a solution--from circuit to compiler-that enables general-purpose use of limited-precision, analog hardwareto accelerate "approximable" code---code that can tolerate imprecise execution. We utilize an algorithmic transformation that automatically converts approximable regions of code from a von Neumann model to an "analog" neural model. We outline the challenges of taking an analog approach, including restricted-range value encoding, limited precision in computation, circuit inaccuracies, noise, and constraints on supported topologies. We address these limitations with a combination of circuit techniques, a hardware/software interface, neuralnetwork training techniques, and compiler support. Analog neural acceleration provides whole application speedup of 3.7x and energy savings of 6.3x with quality loss less than 10% for all except one benchmark. These results show that using limited-precision analog circuits for code acceleration, through a neural a

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Author

Renée St. Amant; Amir Yazdanbakhsh; Jongse Park; Bradley Thwaites; Hadi Esmaeilzadeh; Arjang Hassibi; Luis Ceze; Doug Burger

Publisher

ACM

Published

2014

Language

EN

Field

Computer Science (Physical Sciences)