# On-sensor Printed Machine Learning Classification via Bespoke ADC and Decision Tree Co-Design

arXiv: https://arxiv.org/abs/2312.01172 · HTML: https://mlsanity.com/paper/2312.01172/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 2 Dec 2023.

## Builds on (most cited first, 6 of 6)

- [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)
- [SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing](https://mlsanity.com/paper/1611.05939/) (2016)
- [Model-to-Circuit Cross-Approximation For Printed Machine Learning Classifiers](https://mlsanity.com/paper/2303.08255/) (2023)
- [Approximate Decision Trees For Machine Learning Classification on Tiny Printed Circuits](https://mlsanity.com/paper/2203.08011/) (2022)
- [Hardware-Aware Automated Neural Minimization for Printed Multilayer Perceptrons](https://mlsanity.com/paper/2301.11142/) (2023)

## Built on by (most cited first, 2 of 2)

- [Arbitrary Precision Printed Ternary Neural Networks with Holistic Evolutionary Approximation](https://mlsanity.com/paper/2508.19660/) (2025)
- [Hybrid unary-binary design for multiplier-less printed Machine Learning classifiers](https://mlsanity.com/paper/2509.15316/) (2025)

## Abstract (arXiv)

Printed electronics (PE) technology provides cost-effective hardware with unmet customization, due to their low non-recurring engineering and fabrication costs. PE exhibit features such as flexibility, stretchability, porosity, and conformality, which make them a prominent candidate for enabling ubiquitous computing. Still, the large feature sizes in PE limit the realization of complex printed circuits, such as machine learning classifiers, especially when processing sensor inputs is necessary, mainly due to the costly analog-to-digital converters (ADCs). To this end, we propose the design of fully customized ADCs and present, for the first time, a co-design framework for generating bespoke Decision Tree classifiers. Our comprehensive evaluation shows that our co-design enables self-powered operation of on-sensor printed classifiers in all benchmark cases.