DocumentCode
1180085
Title
Mixed-Signal Approximate Computation: A Neural Predictor Case Study
Author
Amant, Renée St ; Jimenez, D.A. ; Burger, Doug
Author_Institution
Univ. of Texas at Austin, Austin, TX
Volume
29
Issue
1
fYear
2009
Firstpage
104
Lastpage
115
Abstract
As transistors shrink and processors trend toward low power, maintaining precise digital behavior grows more expensive. Replacing digital units with analog equivalents sometimes allows similar computation to be performed at higher speed using less power. As a case study in mixed-signal approximate computation, the authors describe an enhanced neural prediction algorithm and its efficient analog implementation.
Keywords
approximation theory; mixed analogue-digital integrated circuits; analog equivalents; digital units; enhanced neural prediction algorithm; mixed-signal approximate computation; Accuracy; Algorithm design and analysis; Analog circuits; Analog computers; Circuit noise; Computer aided software engineering; High performance computing; History; Prediction algorithms; Wire; analog circuits; approximate computation; computer architecture; imprecise; low power; mixed signal; neural predictor; programmable;
fLanguage
English
Journal_Title
Micro, IEEE
Publisher
ieee
ISSN
0272-1732
Type
jour
DOI
10.1109/MM.2009.10
Filename
4796174
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