research-article

Aging-Aware Training for Printed Neuromorphic Circuits

Authors:

Michael Hefenbrock,

Mehdi B. TahooriAuthors Info & Claims

ICCAD '22: Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design

Article No.: 38, Pages 1 - 9

https://doi.org/10.1145/3508352.3549411

Published: 22 December 2022 Publication History

Abstract

Printed electronics allow for ultra-low-cost circuit fabrication with unique properties such as flexibility, non-toxicity, and stretchability. Because of these advanced properties, there is a growing interest in adapting printed electronics for emerging areas such as fast-moving consumer goods and wearable technologies. In such domains, analog signal processing in or near the sensor is favorable. Printed neuromorphic circuits have been recently proposed as a solution to perform such analog processing natively. Additionally, their learning-based design process allows high efficiency of their optimization and enables them to mitigate the high process variations associated with low-cost printed processes. In this work, we address the aging of the printed components. This effect can significantly degrade the accuracy of printed neuromorphic circuits over time. For this, we develop a stochastic aging-model to describe the behavior of aged printed resistors and modify the training objective by considering the expected loss over the lifetime of the device. This approach ensures to provide acceptable accuracy over the device lifetime. Our experiments show that an overall 35.8% improvement in terms of expected accuracy over the device lifetime can be achieved using the proposed learning approach.

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Cited By

Zhao HSapui BHefenbrock MYang ZBeigl MTahoori M(2023)Highly-Bespoke Robust Printed Neuromorphic Circuits2023 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE56975.2023.10137298(1-6)Online publication date: Apr-2023
https://doi.org/10.23919/DATE56975.2023.10137298
Zhao HHefenbrock MBeigl MTahoori M(2023)Split Additive Manufacturing for Printed Neuromorphic Circuits2023 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE56975.2023.10136891(1-6)Online publication date: Apr-2023
https://doi.org/10.23919/DATE56975.2023.10136891
Zhao HPal PHefenbrock MBeigl MTahoori M(2023)Power-Aware Training for Energy-Efficient Printed Neuromorphic Circuits2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD)10.1109/ICCAD57390.2023.10323917(1-9)Online publication date: 28-Oct-2023
https://doi.org/10.1109/ICCAD57390.2023.10323917
Show More Cited By

Index Terms

Aging-Aware Training for Printed Neuromorphic Circuits
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
2. Hardware
  1. Emerging technologies
    1. Biology-related information processing
      1. Neural systems
  2. Robustness
    1. Hardware reliability
      1. Aging of circuits and systems

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cover image ACM Conferences

ICCAD '22: Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design

October 2022

1467 pages

ISBN:9781450392174

DOI:10.1145/3508352

Conference Chair:
Tulika Mitra
National University of Singapore
,
Program Chairs:
Evangeline Young
The Chinese University of Hong Kong
,
Jinjun Xiong
University at Buffalo (UB)

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Published: 22 December 2022

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Karlsruhe Institute of Technology
Ministry of Science, Research and the Art Baden Wuerttemberg

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ICCAD '22

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ICCAD '22: IEEE/ACM International Conference on Computer-Aided Design

October 30 - November 3, 2022

California, San Diego

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Cited By

Zhao HSapui BHefenbrock MYang ZBeigl MTahoori M(2023)Highly-Bespoke Robust Printed Neuromorphic Circuits2023 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE56975.2023.10137298(1-6)Online publication date: Apr-2023
https://doi.org/10.23919/DATE56975.2023.10137298
Zhao HHefenbrock MBeigl MTahoori M(2023)Split Additive Manufacturing for Printed Neuromorphic Circuits2023 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE56975.2023.10136891(1-6)Online publication date: Apr-2023
https://doi.org/10.23919/DATE56975.2023.10136891
Zhao HPal PHefenbrock MBeigl MTahoori M(2023)Power-Aware Training for Energy-Efficient Printed Neuromorphic Circuits2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD)10.1109/ICCAD57390.2023.10323917(1-9)Online publication date: 28-Oct-2023
https://doi.org/10.1109/ICCAD57390.2023.10323917
Zhao HHefenbrock MBeigl MTahoori M(2023)Highly-dependable printed neuromorphic circuits based on additive manufacturingFlexible and Printed Electronics10.1088/2058-8585/acd8cd8:2(025018)Online publication date: 7-Jun-2023
https://doi.org/10.1088/2058-8585/acd8cd

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