Architecture-Aware Approximate Computing

Mustafa Karakoy, Orhan Kislal, Xulong Tang, Mahmut Taylan Kandemir, Meenakshi Arunachalam

Research output: Contribution to journalArticlepeer-review

Abstract

Observing that many application programs from different domains can live with less-Than-perfect accuracy, existing techniques try to trade off program output accuracy with performance-energy savings. While these works provide point solutions, they leave three critical questions regarding approximate computing unanswered: (i) what is the maximum potential of skipping (i.e., not performing) data accesses under a given inaccuracy bound?; (ii) can we identify the data accesses to drop randomly, or is being architecture aware critical?; and (iii) do two executions that skip the same number of data accesses always result in the same output quality (error)? This paper first provides answers to these questions using ten multithreaded workloads, and then presents a program slicing-based approach that identifies the set of data accesses to drop. Results indicate 8.8% performance improvement and 13.7% energy saving are possible when we set the error bound to 2%, and the corresponding improvements jump to 15% and 25%, respectively, when the error bound is raised to 4%.

Original languageEnglish (US)
Pages (from-to)23-24
Number of pages2
JournalPerformance Evaluation Review
Volume47
Issue number1
DOIs
StatePublished - Dec 17 2019

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

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