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Data clustering for efficient approximate computing

  • Michael G. JordanEmail author
  • Marcelo Brandalero
  • Guilherme M. Malfatti
  • Geraldo F. Oliveira
  • Arthur F. Lorenzon
  • Bruno C. da Silva
  • Luigi Carro
  • Mateus B. Rutzig
  • Antonio Carlos S. Beck
Article
  • 51 Downloads

Abstract

Given the saturation of single-threaded performance improvements in General-Purpose Processor, novel architectural techniques are required to meet emerging demands. In this paper, we propose a generic acceleration framework for approximate algorithms that replaces function execution by table look-up accesses in dedicated memories. A strategy based on the K-Means Clustering algorithm is used to learn mappings from arbitrary function inputs to frequently occurring outputs at compile-time. At run-time, these learned values are fetched from dedicated look-up tables and the best result is selected using the Nearest-Centroid Classifier, which is implemented in hardware. The proposed approach improves over the state-of-the-art neural acceleration solution, with nearly 3X times better performance, \(18.72\%\) up to \(90.99\%\) energy reductions and \(17\%\) area savings under similar levels of quality, thus opening new opportunities for performance harvesting in approximate accelerators.

Keywords

Approximate computing Approximate memoization Data clustering Reuse 

Notes

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Michael G. Jordan
    • 3
    Email author
  • Marcelo Brandalero
    • 3
  • Guilherme M. Malfatti
    • 3
  • Geraldo F. Oliveira
    • 3
  • Arthur F. Lorenzon
    • 1
  • Bruno C. da Silva
    • 3
  • Luigi Carro
    • 3
  • Mateus B. Rutzig
    • 2
  • Antonio Carlos S. Beck
    • 3
  1. 1.Campus AlegreteUniversidade Federal do Pampa (UNIPAMPA)BagéBrazil
  2. 2.Universidade Federal de Santa Maria (UFSM)Santa MariaBrazil
  3. 3.Institute of InformaticsUniversidade Federal do Rio Grande do Sul (UFRGS)Porto AlegreBrazil

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