Neural Acceleration for General-Purpose Approximate Programs

This paper describes a learning-based approach to the acceleration of approximate programs. We describe the \emph{Parrot transformation}, a program transformation that selects and trains a neural network to mimic a region of imperative code. After the learning phase, the compiler replaces the origin...

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Bibliographic Details

Published in:	2012 45th Annual IEEE/ACM International Symposium on Microarchitecture pp. 449 - 460
Main Authors:	Esmaeilzadeh, H., Sampson, A., Ceze, L., Burger, D.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.12.2012
Subjects:	Accelerator Approximate Computing Neural Networks Neural Processing Unit NPU
ISSN:	1072-4451
Online Access:	Get full text
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