# Arbitrary Precision Printed Ternary Neural Networks with Holistic Evolutionary Approximation

arXiv: https://arxiv.org/abs/2508.19660 · HTML: https://mlsanity.com/paper/2508.19660/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 53 references that are in the map.
- First posted on arXiv 27 Aug 2025.

## Builds on (most cited first, 14 of 14)

- [BinaryConnect: Training Deep Neural Networks with binary weights during propagations](https://mlsanity.com/paper/1511.00363/) (2015)
- [Hardware Approximate Techniques for Deep Neural Network Accelerators: A Survey](https://mlsanity.com/paper/2203.08737/) (2022)
- [Cross-Layer Approximation For Printed Machine Learning Circuits](https://mlsanity.com/paper/2203.05915/) (2022)
- [Co-Design of Approximate Multilayer Perceptron for Ultra-Resource Constrained Printed Circuits](https://mlsanity.com/paper/2302.14576/) (2023)
- [Approximate Computing and the Efficient Machine Learning Expedition](https://mlsanity.com/paper/2210.00497/) (2022)
- [Bespoke Approximation of Multiplication-Accumulation and Activation Targeting Printed Multilayer Perceptrons](https://mlsanity.com/paper/2312.17612/) (2023)
- [Embedding Hardware Approximations in Discrete Genetic-based Training for Printed MLPs](https://mlsanity.com/paper/2402.02930/) (2024)
- [Evolutionary Approximation of Ternary Neurons for On-sensor Printed Neural Networks](https://mlsanity.com/paper/2407.20589/) (2024)
- [On-sensor Printed Machine Learning Classification via Bespoke ADC and Decision Tree Co-Design](https://mlsanity.com/paper/2312.01172/) (2023)
- [Design and In-training Optimization of Binary Search ADC for Flexible Classifiers](https://mlsanity.com/paper/2410.00737/) (2024)
- [Optimization of BDD-based Approximation Error Metrics Calculations](https://mlsanity.com/paper/2205.03267/) (2022)
- [Reducing ADC Front-end Costs During Training of On-sensor Printed Multilayer Perceptrons](https://mlsanity.com/paper/2411.08674/) (2024)
- [AxMED: Formal Analysis and Automated Design of Approximate Median Filters using BDDs](https://mlsanity.com/paper/2502.18174/) (2025)
- [Computing with Printed and Flexible Electronics](https://mlsanity.com/paper/2505.00011/) (2025)

## Abstract (arXiv)

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.