Publication Details
Evolutionary Approximation of Ternary Neurons for On-sensor Printed Neural Networks
Kokkinis Argyrios (AUTH)
Papanikolaou Panagiotis (UMICH)
Vašíček Zdeněk, doc. Ing., Ph.D. (DCSY FIT BUT)
Siozios Kostas (AUTH)
Tzimpragos Georgios (UMICH)
Tahoori Mehdi (KIT)
Zervakis Georgios (UPATRAS)
Approximate Computing, Electrolyte-gated FET, Printed Electronics, Low-Power Classifiers, Ternary Neural Networks
Printed electronics offer ultra-low manufacturing costs and the potential for on-demand fabrication of flexible hardware. However, significant intrinsic constraints stemming from their large feature sizes and low integration density pose design challenges that hinder their practicality. In this work, we conduct a holistic exploration of printed neural network accelerators, starting from the analog-to-digital interface---a major area and power sink for sensor processing applications---and extending to networks of ternary neurons and their implementation. We propose bespoke ternary neural networks using approximate popcount and popcount-compare units, developed through a multi-phase evolutionary optimization approach and interfaced with sensors via customizable analog-to-binary converters. Our evaluation results show that the presented designs outperform the state of the art, achieving at least 6x improvement in area and 19x in power. To our knowledge, they represent the first open-source digital printed neural network classifiers capable of operating with existing printed energy harvesters.
@INPROCEEDINGS{FITPUB13206,
author = "Vojt\v{e}ch Mr\'{a}zek and Argyrios Kokkinis and Panagiotis Papanikolaou and Zden\v{e}k Va\v{s}\'{i}\v{c}ek and Kostas Siozios and Georgios Tzimpragos and Mehdi Tahoori and Georgios Zervakis",
title = "Evolutionary Approximation of Ternary Neurons for On-sensor Printed Neural Networks",
pages = "1--9",
booktitle = "2024 IEEE/ACM International Conference on Computer Aided Design (ICCAD)",
year = 2024,
location = "New York, US",
publisher = "Association for Computing Machinery",
ISBN = "979-8-4007-1077-3",
doi = "10.1145/3676536.3676728",
language = "english",
url = "https://www.fit.vut.cz/research/publication/13206"
}