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Low-cost and efficient prediction hardware for tabular data using tiny classifier circuits

Computer Science

Low-cost and efficient prediction hardware for tabular data using tiny classifier circuits

K. Iordanou, T. Atkinson, et al.

This innovative research by Konstantinos Iordanou, Timothy Atkinson, Emre Ozer, Jedrzej Kufel, Grace Aligada, John Biggs, Gavin Brown, and Mikel Luján presents a groundbreaking methodology for automatically generating tiny predictor circuits for tabular data classification. With a focus on maximizing accuracy while minimizing hardware and power usage, this study reveals that these compact classifiers can significantly outperform traditional machine learning techniques in efficiency.

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Playback language: English
Abstract
This paper introduces a methodology for automatically generating predictor circuits for tabular data classification. The approach uses an evolutionary algorithm to design classifier circuits with maximized accuracy using minimal hardware resources and power. Simulation results show that these "tiny classifiers" utilize significantly less area and power than comparable machine learning techniques when implemented on both silicon and flexible substrates.
Publisher
Nature Electronics
Published On
May 01, 2024
Authors
Konstantinos Iordanou, Timothy Atkinson, Emre Ozer, Jedrzej Kufel, Grace Aligada, John Biggs, Gavin Brown, Mikel Luján
Tags
predictor circuits
tabular data classification
evolutionary algorithm
tiny classifiers
machine learning
hardware efficiency
power optimization

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