SiMul: An Algorithm-Driven Approximate Multiplier Design for Machine Learning

Zhenhong Liu, Amir Yazdanbakhsh, Taejoon Park, Hadi Esmaeilzadeh, Nam Sung Kim

Research output: Contribution to journalArticlepeer-review


The need to support various machine learning (ML) algorithms on energy-constrained computing devices has steadily grown. In this article, we propose an approximate multiplier, which is a key hardware component in various ML accelerators. Dubbed SiMul, our approximate multiplier features user-controlled precision that exploits the common characteristics of ML algorithms. SiMul supports a tradeoff between compute precision and energy consumption at runtime, reducing the energy consumption of the accelerator while satisfying a desired inference accuracy requirement. Compared with a precise multiplier, SiMul improves the energy efficiency of multiplication by 11.6x to 3.2x while achieving 81.7-percent to 98.5-percent precision for individual multiplication operations (96.0-, 97.8-, and 97.7-percent inference accuracy for three distinct applications, respectively, compared to the baseline inference accuracy of 98.3, 99.0, and 97.7 percent using precise multipliers). A neural accelerator implemented with our multiplier can provide 1.7x (up to 2.1x) higher energy efficiency over one implemented with the precise multiplier with a negligible impact on the accuracy of the output for various applications.

Original languageEnglish (US)
Article number8430625
Pages (from-to)50-59
Number of pages10
JournalIEEE Micro
Issue number4
StatePublished - Jul 1 2018


  • approximate computing
  • hardware
  • machine learning
  • multiplier
  • neural network

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Electrical and Electronic Engineering


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