# Late Breaking Results: Energy-Efficient Printed Machine Learning Classifiers with Sequential SVMs

arXiv: https://arxiv.org/abs/2501.16828 · HTML: https://mlsanity.com/paper/2501.16828/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 4 references that are in the map.
- First posted on arXiv 28 Jan 2025.

## Builds on (most cited first, 2 of 2)

- [Co-Design of Approximate Multilayer Perceptron for Ultra-Resource Constrained Printed Circuits](https://mlsanity.com/paper/2302.14576/) (2023)
- [Model-to-Circuit Cross-Approximation For Printed Machine Learning Classifiers](https://mlsanity.com/paper/2303.08255/) (2023)

## Abstract (arXiv)

Printed Electronics (PE) provide a mechanically flexible and cost-effective solution for machine learning (ML) circuits, compared to silicon-based technologies. However, due to large feature sizes, printed classifiers are limited by high power, area, and energy overheads, which restricts the realization of battery-powered systems. In this work, we design sequential printed bespoke Support Vector Machine (SVM) circuits that adhere to the power constraints of existing printed batteries while minimizing energy consumption, thereby boosting battery life. Our results show 6.5x energy savings while maintaining higher accuracy compared to the state of the art.