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Issue No. 01 - January/February (2009 vol. 29)
ISSN: 0272-1732
pp: 104-115
Daniel A. Jiménez , University of Texas at San Antonio
Renée St. Amant , University of Texas at Austin
Doug Burger , Microsoft Research
ABSTRACT
<p>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.</p>
INDEX TERMS
computer architecture, low power, mixed signal, programmable, analog circuits, approximate computation, imprecise, neural predictor
CITATION
Daniel A. Jiménez, Renée St. Amant, Doug Burger, "Mixed-Signal Approximate Computation: A Neural Predictor Case Study", IEEE Micro, vol. 29, no. , pp. 104-115, January/February 2009, doi:10.1109/MM.2009.10
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