Minimum Precision Requirements of General Margin Hyperplane Classifiers

Charbel Sakr, Yongjune Kim, Naresh Shanbhag

Research output: Contribution to journalArticlepeer-review


Margin hyperplane classifiers such as support vector machines have achieved considerable success in various classification tasks. Their simplicity makes them suitable candidates for the design of embedded intelligent systems. Precision is an effective parameter to trade-off accuracy and resource utilization. We present analytical bounds on the precision requirements of general margin hyperplane classifiers. In addition, we propose a principled precision reduction scheme based on the trade-off between input and weight precisions. We present simulation results that support our analysis and illustrate the gains of our approach in terms of reducing resource utilization. For instance, we show that a linear margin classifier with precision assignment dictated by our approach and applied to the 'two versus four' task of the MNIST dataset is \sim 2\times more accurate than a standard 8 bits low precision implementation in spite of using \sim 2\times 10^{4} fewer 1 bit full adders and \sim 2\times 10^{3} fewer bits for data and weight representation.

Original languageEnglish (US)
Article number8685184
Pages (from-to)253-266
Number of pages14
JournalIEEE Journal on Emerging and Selected Topics in Circuits and Systems
Issue number2
StatePublished - Jun 2019


  • Fixed-point
  • accuracy
  • precision
  • resource constrained machine learning

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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