Cited time in webofscience Cited time in scopus

Energy-Efficient Approximate Multiplication for Digital Signal Processing and Classification Applications

Title
Energy-Efficient Approximate Multiplication for Digital Signal Processing and Classification Applications
Author(s)
Narayanamoorthy, SrinivasanMoghaddam, Hadi AsghariLiu, Zhenhong박태준Kim, Nam Sung
Issued Date
2015-06
Citation
IEEE Transactions on Very Large Scale Integration (VLSI) Systems, v.23, no.6, pp.1180 - 1184
Type
Article
Author Keywords
Approximationenergy efficiencymultiplication
Keywords
Accuracy of ClassificationsApproximationComputational AccuracyComputational ErrorDigital DevicesDigital Signal ProcessingDigital Signal Processing (DSP)Energy EfficiencyEnergy UtilizationERRORErrorsFixed Point ArithmeticMatrix MultiplicationMultiplicationMultiplier ArchitectureSignal Processing
ISSN
1063-8210
Abstract
The need to support various digital signal processing (DSP) and classification applications on energy-constrained devices has steadily grown. Such applications often extensively perform matrix multiplications using fixed-point arithmetic while exhibiting tolerance for some computational errors. Hence, improving the energy efficiency of multiplications is critical. In this brief, we propose multiplier architectures that can tradeoff computational accuracy with energy consumption at design time. Compared with a precise multiplier, the proposed multiplier can consume 58% less energy/op with average computational error of ∼ 1 %. Finally, we demonstrate that such a small computational error does not notably impact the quality of DSP and the accuracy of classification applications. © 1993-2012 IEEE.
URI
http://hdl.handle.net/20.500.11750/2895
DOI
10.1109/TVLSI.2014.2333366
Publisher
Institute of Electrical and Electronics Engineers Inc.
Files in This Item:

There are no files associated with this item.

Appears in Collections:
Department of Electrical Engineering and Computer Science Information and Communication Engineering Research Center 1. Journal Articles

qrcode

  • twitter
  • facebook
  • mendeley

Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE