# Model-to-Circuit Cross-Approximation For Printed Machine Learning Classifiers

arXiv: https://arxiv.org/abs/2303.08255 · HTML: https://mlsanity.com/paper/2303.08255/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 42 references that are in the map.
- First posted on arXiv 14 Mar 2023.

## Builds on (most cited first, 3 of 3)

- [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)
- [FixyNN: Efficient Hardware for Mobile Computer Vision via Transfer Learning](https://mlsanity.com/paper/1902.11128/) (2019)

## Built on by (most cited first, 7 of 7)

- [Bespoke Approximation of Multiplication-Accumulation and Activation Targeting Printed Multilayer Perceptrons](https://mlsanity.com/paper/2312.17612/) (2023)
- [Approximate Computing Survey, Part II: Application-Specific & Architectural Approximation Techniques and Applications](https://mlsanity.com/paper/2307.11128/) (2023)
- [Embedding Hardware Approximations in Discrete Genetic-based Training for Printed MLPs](https://mlsanity.com/paper/2402.02930/) (2024)
- [On-sensor Printed Machine Learning Classification via Bespoke ADC and Decision Tree Co-Design](https://mlsanity.com/paper/2312.01172/) (2023)
- [Late Breaking Results: Energy-Efficient Printed Machine Learning Classifiers with Sequential SVMs](https://mlsanity.com/paper/2501.16828/) (2025)
- [Compact Yet Highly Accurate Printed Classifiers Using Sequential Support Vector Machine Circuits](https://mlsanity.com/paper/2502.01498/) (2025)
- [Hybrid unary-binary design for multiplier-less printed Machine Learning classifiers](https://mlsanity.com/paper/2509.15316/) (2025)

## Abstract (arXiv)

Printed electronics (PE) promises on-demand fabrication, low non-recurring engineering costs, and sub-cent fabrication costs. It also allows for high customization that would be infeasible in silicon, and bespoke architectures prevail to improve the efficiency of emerging PE machine learning (ML) applications. Nevertheless, large feature sizes in PE prohibit the realization of complex ML models in PE, even with bespoke architectures. In this work, we present an automated, cross-layer approximation framework tailored to bespoke architectures that enable complex ML models, such as Multi-Layer Perceptrons (MLPs) and Support Vector Machines (SVMs), in PE. Our framework adopts cooperatively a hardware-driven coefficient approximation of the ML model at algorithmic level, a netlist pruning at logic level, and a voltage over-scaling at the circuit level. Extensive experimental evaluation on 12 MLPs and 12 SVMs and more than 6000 approximate and exact designs demonstrates that our model-to-circuit cross-approximation delivers power and area optimal designs that, compared to the state-of-the-art exact designs, feature on average 51% and 66% area and power reduction, respectively, for less than 5% accuracy loss. Finally, we demonstrate that our framework enables 80% of the examined classifiers to be battery-powered with almost identical accuracy with the exact designs, paving thus the way towards smart complex printed applications.