Reconfigurable Support Vector Machine Classifier with Approximate Computing

Authors: Leussen, Martin Van and Huisken, Jos and Wang, Lei and Jiao, Hailong and de Gyvez, José Pineda

Abstract::Support Vector Machine (SVM) is one of the most popular machine learning algorithms. An energy-efficient SVM classifier is proposed in this paper, where approximate computing is utilized to reduce energy consumption and silicon area. A hardware architecture with reconfigurable kernels and overflow-resilient limiter is presented. For different applications, different kernels can be chosen and configured to achieve the optimum energy efficiency while achieving the performance requirement. For an epileptic seizure detection application, on average, 15% energy and 14% area savings are achieved with the proposed approximate SVM classifier compared to a fully-accurate SVM implementation with almost no accuracy degradation.

[BibTeX] [ DOI]
@inproceedings{Leussen2017SVM,
  author = {Leussen, Martin Van and Huisken, Jos and Wang, Lei and Jiao, Hailong and de Gyvez, Jos{\'e} Pineda},
  booktitle = {IEEE Computer Society Annual Symposium on VLSI (ISVLSI)},
  title = {Reconfigurable Support Vector Machine Classifier with Approximate Computing},
  year = {2017},
  volume = {3},
  number = {},
  pages = {13-18},
  keywords = {energy conservation;pattern classification;support vector machines;SVM;approximate computing;energy consumption reduction;energy-efficient SVM classifier;hardware architecture;machine learning algorithms;overflow-resilient limiter;reconfigurable kernels;reconfigurable support vector machine classifier;silicon area reduction;Approximate computing;Computer architecture;Hardware;Kernel;Support vector machine classification;Training;Machine learning;approximate multiplier;energy efficiency;reconfigurable architecture},
  doi = {10.1109/ISVLSI.2017.13},
  issn = {},
  month = jul,
  url = {https://research.tue.nl/files/78140508/07987488.pdf},
  organization = {IEEE}
}