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Aging-Aware Training for Printed Neuromorphic Circuits

Zhao, Haibin ORCID iD icon 1; Hefenbrock, Michael 1; Beigl, Michael 1; Tahoori, Mehdi B. 2
1 Institut für Telematik (TM), Karlsruher Institut für Technologie (KIT)
2 Institut für Technische Informatik (ITEC), Karlsruher Institut für Technologie (KIT)

Abstract (englisch):

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 propose a learning-based approach to address another major challenge of printed electronics, namely 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. ... mehr

Postprint §
DOI: 10.5445/IR/1000150219/post
Veröffentlicht am 04.10.2023
Preprint §
DOI: 10.5445/IR/1000150219
Veröffentlicht am 05.09.2022
DOI: 10.1145/3508352.3549411
Zitationen: 4
Zitationen: 4
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Technische Informatik (ITEC)
Institut für Telematik (TM)
Publikationstyp Proceedingsbeitrag
Publikationsdatum 03.10.2022
Sprache Englisch
Identifikator ISBN: 987-1-4503-9217-4
KITopen-ID: 1000150219
Erschienen in International Conference on Computer Aided Design (ICCAD ’22), October 30-November 3, 2022, San Diego, CA, USA
Veranstaltung 41st International Conference on Computer Aided Design (ICCAD 2022), San Diego, CA, USA, 30.10.2022 – 03.11.2022
Verlag Association for Computing Machinery (ACM)
Seiten Art.-No.: 38
Projektinformation MERAGEM (MWK, 17861 (intern))
Schlagwörter printed electronics, neuromorphic computing, aging model, aging-aware training
Nachgewiesen in Scopus
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