# Co-Design of Approximate Multilayer Perceptron for Ultra-Resource Constrained Printed Circuits

arXiv: https://arxiv.org/abs/2302.14576 · HTML: https://mlsanity.com/paper/2302.14576/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 32 references that are in the map.
- First posted on arXiv 28 Feb 2023.

## Builds on (most cited first, 4 of 4)

- [ALWANN: Automatic Layer-Wise Approximation of Deep Neural Network Accelerators without Retraining](https://mlsanity.com/paper/1907.07229/) (2019)
- [Cross-Layer Approximation For Printed Machine Learning Circuits](https://mlsanity.com/paper/2203.05915/) (2022)
- [Control Variate Approximation for DNN Accelerators](https://mlsanity.com/paper/2102.09642/) (2021)
- [CFU Playground: Full-Stack Open-Source Framework for Tiny Machine Learning (tinyML) Acceleration on FPGAs](https://mlsanity.com/paper/2201.01863/) (2022)

## Built on by (most cited first, 11 of 11)

- [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)
- [Exploration of Low-Power Flexible Stress Monitoring Classifiers for Conformal Wearables](https://mlsanity.com/paper/2508.19661/) (2025)
- [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)
- [Arbitrary Precision Printed Ternary Neural Networks with Holistic Evolutionary Approximation](https://mlsanity.com/paper/2508.19660/) (2025)
- [MaRVIn: A Cross-Layer Mixed-Precision RISC-V Framework for DNN Inference, from ISA Extension to Hardware Acceleration](https://mlsanity.com/paper/2509.15187/) (2025)
- [Hybrid unary-binary design for multiplier-less printed Machine Learning classifiers](https://mlsanity.com/paper/2509.15316/) (2025)
- [Bespoke Co-processor for Energy-Efficient Health Monitoring on RISC-V-based Flexible Wearables](https://mlsanity.com/paper/2511.05985/) (2025)

## Abstract (arXiv)

Printed Electronics (PE) exhibits on-demand, extremely low-cost hardware due to its additive manufacturing process, enabling machine learning (ML) applications for domains that feature ultra-low cost, conformity, and non-toxicity requirements that silicon-based systems cannot deliver. Nevertheless, large feature sizes in PE prohibit the realization of complex printed ML circuits. In this work, we present, for the first time, an automated printed-aware software/hardware co-design framework that exploits approximate computing principles to enable ultra-resource constrained printed multilayer perceptrons (MLPs). Our evaluation demonstrates that, compared to the state-of-the-art baseline, our circuits feature on average 6x (5.7x) lower area (power) and less than 1% accuracy loss.