# Bespoke Approximation of Multiplication-Accumulation and Activation Targeting Printed Multilayer Perceptrons

arXiv: https://arxiv.org/abs/2312.17612 · HTML: https://mlsanity.com/paper/2312.17612/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 29 Dec 2023.

## Builds on (most cited first, 10 of 10)

- [Hardware Approximate Techniques for Deep Neural Network Accelerators: A Survey](https://mlsanity.com/paper/2203.08737/) (2022)
- [Mix and Match: A Novel FPGA-Centric Deep Neural Network Quantization Framework](https://mlsanity.com/paper/2012.04240/) (2020)
- [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)
- [HybridDNN: A Framework for High-Performance Hybrid DNN Accelerator Design and Implementation](https://mlsanity.com/paper/2004.03804/) (2020)
- [Approximate Computing and the Efficient Machine Learning Expedition](https://mlsanity.com/paper/2210.00497/) (2022)
- [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)
- [Tiny Classifier Circuits: Evolving Accelerators for Tabular Data](https://mlsanity.com/paper/2303.00031/) (2023)

## Built on by (most cited first, 4 of 4)

- [Exploration of Low-Power Flexible Stress Monitoring Classifiers for Conformal Wearables](https://mlsanity.com/paper/2508.19661/) (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)
- [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) feature distinct and remarkable characteristics that make them a prominent technology for achieving true ubiquitous computing. This is particularly relevant in application domains that require conformal and ultra-low cost solutions, which have experienced limited penetration of computing until now. Unlike silicon-based technologies, PE offer unparalleled features such as non-recurring engineering costs, ultra-low manufacturing cost, and on-demand fabrication of conformal, flexible, non-toxic, and stretchable hardware. However, PE face certain limitations due to their large feature sizes, that impede the realization of complex circuits, such as machine learning classifiers. In this work, we address these limitations by leveraging the principles of Approximate Computing and Bespoke (fully-customized) design. We propose an automated framework for designing ultra-low power Multilayer Perceptron (MLP) classifiers which employs, for the first time, a holistic approach to approximate all functions of the MLP's neurons: multiplication, accumulation, and activation. Through comprehensive evaluation across various MLPs of varying size, our framework demonstrates the ability to enable battery-powered operation of even the most intricate MLP architecture examined, significantly surpassing the current state of the art.