Arbitrary Precision Printed Ternary Neural Networks with Holistic Evolutionary Approximation

TL;DR

This paper introduces a holistic framework for designing printed ternary neural networks with arbitrary precision, significantly improving area and power efficiency while enabling battery-powered operation with minimal accuracy loss.

Contribution

It presents the first automated design framework for printed Ternary Neural Networks with arbitrary input precision, optimizing for area, power, and accuracy in printed electronics.

Findings

Circuits outperform existing methods by 17x in area.

Power consumption reduced by 59x on average.

Supports printed-battery-powered operation with less than 5% accuracy loss.

Abstract

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.