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Hardware–Software Co-design for Ultra-Resource-Constrained Embedded Machine Learning Inference: A Printed Electronics Use Case

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Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing

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Abstract

In this chapter, we discuss hardware–software co-design approaches that enable machine learning inference on ultra-resource-constrained embedded systems. As our use case, we consider the printed electronics. The latter form an extreme example of embedded machine learning application. Printed electronics constitute a promising solution to bring computing and smart services in application domains that require sub-cent cost and conformality and have not seen yet significant penetration of computing. Printed electronics form a rapidly growing market but also feature several prevalent limitations. Integration density and performance of printed electronics are of order of magnitude lower than those in silicon VLSI systems, and implementing complex circuits, e.g., ML classifiers, poses a great challenge. Considering the a priori requirement of embedded ML for acceptable accuracy and low latency within the limitations of the ultra-resource-constrained printed devices, custom-designed circuits as well as software–hardware co-design and optimization combined with non-conventional computing approaches (e.g., approximate and stochastic computing) are becoming mandatory for the realization of such circuits.

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  1. 1.

    https://github.com/PrintedComputing.

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Acknowledgements

This work is partially supported by the German Research Foundation (DFG) through the project “ACCROSS: Approximate Computing aCROss the System Stack” HE 2343/16-1 and by grant from the Excellence Initiative of Karlsruhe Institute of Technology under Future Field program “SoftNeuro.”

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Authors and Affiliations

  1. Karlsruhe Institute of Technology, Karlsruhe, Germany

    Georgios Zervakis, Mehdi B. Tahoori & Jörg Henkel

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  1. Georgios Zervakis
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  2. Mehdi B. Tahoori
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  3. Jörg Henkel
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Correspondence to Georgios Zervakis .

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Editors and Affiliations

  1. Colorado State University, Fort Collins, CO, USA

    Sudeep Pasricha

  2. New York University Abu Dhabi, Abu Dhabi, Abu Dhabi, United Arab Emirates

    Muhammad Shafique

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Zervakis, G., Tahoori, M.B., Henkel, J. (2024). Hardware–Software Co-design for Ultra-Resource-Constrained Embedded Machine Learning Inference: A Printed Electronics Use Case. In: Pasricha, S., Shafique, M. (eds) Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-39932-9_8

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  • DOI: https://doi.org/10.1007/978-3-031-39932-9_8

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