Approximate Computing Methods for Embedded Machine Learning

@article{Ibrahim2018ApproximateCM,
  title={Approximate Computing Methods for Embedded Machine Learning},
  author={Ali Ibrahim and Mario Osta and Mohamad Gabriel Alameh and Moustafa Saleh and Hussein Chible and Maurizio Valle},
  journal={2018 25th IEEE International Conference on Electronics, Circuits and Systems (ICECS)},
  year={2018},
  pages={845-848}
}
  • A. Ibrahim, M. Osta, M. Valle
  • Published 1 December 2018
  • Computer Science
  • 2018 25th IEEE International Conference on Electronics, Circuits and Systems (ICECS)
Embedding Machine Learning enables integrating intelligence in recent application domains such as Internet of Things, portable healthcare systems, and wearable devices. This paper presents an assessment of approximate computing methods at algorithmic, architecture, and circuit levels and draws perspectives for further developments and applications. The main goal is to investigate how approximate computing may reduce the complexity and enable the feasibility of embedded Machine Learning (ML… 

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