Publication Details
Arbitrary Precision Printed Ternary Neural Networks with Holistic Evolutionary Approximation
Balaskas Konstantinos (UPATRAS)
Duarte Carolina Lozano Paula (KIT)
Vašíček Zdeněk, doc. Ing., Ph.D. (DCSY FIT BUT)
Tahoori Mehdi (KIT)
Zervakis Georgios (UPATRAS)
Printed electronic, approximate computing, evolutionary optimization
Printed electronics offer a promising alternative for applications beyond silicon-based systems, requiring properties like flexibility, stretchability, conformality, and ultra-low fabrication costs. Despite the large feature sizes in printed electronics, printed neural networks have attracted attention for meeting target application requirements, though realizing complex circuits remains challenging. This work bridges the gap between classification accuracy and area efficiency in printed neural networks, covering the entire processing-near-sensor system design and co-optimization from the analog-to-digital interface- a major area and power bottleneck-to the digital classifier. We propose an automated framework for designing printed Ternary Neural Networks with arbitrary input precision, utilizing multi-objective optimization and holistic approximation. Our circuits outperform existing approximate printed neural networks by 17x in area and 59x in power on average, being the first to enable printed-battery-powered operation with under 5% accuracy loss while accounting for analog-to-digital interfacing costs.
@ARTICLE{FITPUB13265,
author = "Vojt\v{e}ch Mr\'{a}zek and Konstantinos Balaskas and Paula Lozano Carolina Duarte and Zden\v{e}k Va\v{s}\'{i}\v{c}ek and Mehdi Tahoori and Georgios Zervakis",
title = "Arbitrary Precision Printed Ternary Neural Networks with Holistic Evolutionary Approximation",
pages = "1--13",
journal = "IEEE Transactions on Circuits and Systems for Artificial Intelligence",
year = 2025,
ISSN = "2996-6647",
doi = "10.1109/TCASAI.2025.3604384",
language = "english",
url = "https://www.fit.vut.cz/research/publication/13265"
}