mlsanityData to 7 Oct 2026

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

arXiv 2312.17612 ↗First posted 29 Dec 2023cs.AR cs.LGcited by 4

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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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 10 of its 29 references that are in the map. First posted on arXiv 29 Dec 2023.

Builds on

10 of 29 references.

References · 10

  1. 2022Hardware Approximate Techniques for Deep Neural Network Accelerators: A Surveycited by 39
  2. 2020Mix and Match: A Novel FPGA-Centric Deep Neural Network Quantization Frameworkcited by 27
  3. 2022Cross-Layer Approximation For Printed Machine Learning Circuitscited by 12
  4. 2023Co-Design of Approximate Multilayer Perceptron for Ultra-Resource Constrained Printed Circuitscited by 11
  5. 2020HybridDNN: A Framework for High-Performance Hybrid DNN Accelerator Design and Implementationcited by 8
  6. 2022Approximate Computing and the Efficient Machine Learning Expeditioncited by 8
  7. 2023Model-to-Circuit Cross-Approximation For Printed Machine Learning Classifierscited by 7
  8. 2022Approximate Decision Trees For Machine Learning Classification on Tiny Printed Circuitscited by 3
  9. 2023Hardware-Aware Automated Neural Minimization for Printed Multilayer Perceptronscited by 3
  10. 2023Tiny Classifier Circuits: Evolving Accelerators for Tabular Datacited by 1

Built on by

Cited by year

Citing papers per year: 2025: 4.

4 papers.

  1. 2025Exploration of Low-Power Flexible Stress Monitoring Classifiers for Conformal Wearablescited by 1
  2. 2025Compact Yet Highly Accurate Printed Classifiers Using Sequential Support Vector Machine Circuitscited by 0
  3. 2025Arbitrary Precision Printed Ternary Neural Networks with Holistic Evolutionary Approximationcited by 0
  4. 2025Bespoke Co-processor for Energy-Efficient Health Monitoring on RISC-V-based Flexible Wearablescited by 0