Energy-efficient approximate multiplication for digital signal processing and classification applications

Srinivasan Narayanamoorthy, Hadi Asghari Moghaddam, Zhenhong Liu, Taejoon Park, Nam Sung Kim

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number6858039
Pages (from-to)1180-1184
Number of pages5
JournalIEEE Transactions on Very Large Scale Integration (VLSI) Systems
Volume23
Issue number6
DOIs
StatePublished - Jun 1 2015
Externally publishedYes

Keywords

  • Approximation
  • Multiplication
  • energy efficiency

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Electrical and Electronic Engineering

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