A Low-Power Analog Integrated Implementation of the Support Vector Machine Algorithm with On-Chip Learning Tested on

Vassilis Alimisis1, Georgios Gennis1, Marios Gourdouparis1

  • 1Department of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece.

Summary

This study presents an autonomous, on-chip learning hardware-friendly support vector machine (SVM) classifier. Achieving high accuracy with ultra-low power consumption (72 μW) using subthreshold techniques.

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