Compact Yet Highly Accurate Printed Classifiers Using Sequential Support Vector Machine Circuits

TL;DR

This paper introduces a novel sequential SVM classifier for printed electronics that significantly reduces area and power consumption while maintaining high accuracy, advancing the feasibility of complex printed ML classifiers.

Contribution

It presents the first sequential SVM design for printed electronics, achieving superior area efficiency and accuracy compared to existing printed ML classifiers.

Findings

6x lower area than state-of-the-art printed classifiers

4.6% higher accuracy than previous printed solutions

First implementation of sequential SVMs in printed electronics

Abstract

Printed Electronics (PE) technology has emerged as a promising alternative to silicon-based computing. It offers attractive properties such as on-demand ultra-low-cost fabrication, mechanical flexibility, and conformality. However, PE are governed by large feature sizes, prohibiting the realization of complex printed Machine Learning (ML) classifiers. Leveraging PE's ultra-low non-recurring engineering and fabrication costs, designers can fully customize hardware to a specific ML model and dataset, significantly reducing circuit complexity. Despite significant advancements, state-of-the-art solutions achieve area efficiency at the expense of considerable accuracy loss. Our work mitigates this by designing area- and power-efficient printed ML classifiers with little to no accuracy degradation. Specifically, we introduce the first sequential Support Vector Machine (SVM) classifiers, exploiting the hardware efficiency of bespoke control and storage units and a single Multiply-Accumulate compute engine. Our SVMs yield on average 6x lower area and 4.6% higher accuracy compared to the printed state of the art.