Arbitrary Precision Printed Ternary Neural Networks with Holistic Evolutionary Approximation

Arbitrary Precision Printed Ternary Neural Networks with Holistic Evolutionary Approximation

Článek recenzovaný mimo WoS a Scopus

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 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

Printed electronic, approximate computing, evolutionary optimization

MRÁZEK, V.; BALASKAS, K.; DUARTE, P.; VAŠÍČEK, Z.; TAHOORI, M.; ZERVAKIS, G.

2026

01.10.2025

IEEE Transactions on Circuits and Systems for Artificial Intelligence

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4

Spojené státy americké

351

363

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https://ieeexplore.ieee.org/document/11145783