Result Details
Arbitrary Precision Printed Ternary Neural Networks with Holistic Evolutionary Approximation
Balaskas Konstantinos
Duarte Paula Carolina Lozano
Vašíček Zdeněk, doc. Ing., Ph.D., DCSY (FIT)
Tahoori Mehdi
Zervakis Georgios
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.
Printed electronic, approximate computing, evolutionary optimization
@article{BUT191361,
author="Vojtěch {Mrázek} and {} and {} and Zdeněk {Vašíček} and {} and {}",
title="Arbitrary Precision Printed Ternary Neural Networks with Holistic Evolutionary Approximation",
journal="IEEE Transactions on Circuits and Systems for Artificial Intelligence",
year="2025",
volume="2",
number="4",
pages="351--363",
doi="10.1109/TCASAI.2025.3604384",
url="https://ieeexplore.ieee.org/document/11145783"
}