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Mixed-Signal Approximate Computation: A Neural Predictor Case Study
January/February 2009 (vol. 29 no. 1)
pp. 104-115
Renée St. Amant, University of Texas at Austin
Daniel A. Jiménez, University of Texas at San Antonio
Doug Burger, Microsoft Research

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.

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Index Terms:
computer architecture, low power, mixed signal, programmable, analog circuits, approximate computation, imprecise, neural predictor
Renée St. Amant, Daniel A. Jiménez, Doug Burger, "Mixed-Signal Approximate Computation: A Neural Predictor Case Study," IEEE Micro, vol. 29, no. 1, pp. 104-115, Jan.-Feb. 2009, doi:10.1109/MM.2009.10
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