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Neural Acceleration for General-Purpose Approximate Programs
May-June 2013 (vol. 33 no. 3)
pp. 16-27
Hadi Esmaeilzadeh, University of Washington
Adrian Sampson, University of Washington
Luis Ceze, University of Washington
Doug Burger, Microsoft Research
This work proposes an approximate algorithmic transformation and a new class of accelerators, called neural processing units (NPUs). NPUs leverage the approximate algorithmic transformation that converts regions of code from a Von Neumann model to a neural model. NPUs achieve an average 2.3× speedup and 3.0× energy savings for general-purpose approximate programs. This new class of accelerators shows that significant performance and efficiency gains are possible when the abstraction of full accuracy is relaxed in general-purpose computing.
Index Terms:
Computer architecture,Neural networks,Approximation algorithms,Algorithm design and analysis,Accelerators,NPUs,approximate computing,accelerators,neural networks,Parrot algorithmic transformation,neural processing units
Citation:
Hadi Esmaeilzadeh, Adrian Sampson, Luis Ceze, Doug Burger, "Neural Acceleration for General-Purpose Approximate Programs," IEEE Micro, vol. 33, no. 3, pp. 16-27, May-June 2013, doi:10.1109/MM.2013.28
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