The field of neuromorphic computing has been rapidly evolving in recent years, with an increasing focus on hardware design and reliability. This special session paper provides an overview of the recent developments in neuromorphic computing, focusing on hardware design and reliability. We first review the traditional CMOS-based approaches to neuromorphic hardware design and identify the challenges related to scalability, latency, and power consumption. We then investigate alternative approaches based on emerging technologies, specifically integrated photonics approaches within the NEUROPULS project. Finally, we examine the impact of device variability and aging on the reliability of neuromorphic hardware and present techniques for mitigating these effects. This review is intended to serve as a valuable resource for researchers and practitioners in neuromorphic computing.
Special Session: Neuromorphic hardware design and reliability from traditional CMOS to emerging technologies / Pavanello, Fabio; Ioana Vatajelu, Elena; Bosio, Alberto; Van Vaerenbergh, Thomas; Bienstman, Peter; Charbonnier, Benoit; Carpegna, Alessio; DI CARLO, Stefano; Savino, Alessandro. - ELETTRONICO. - (2023), pp. 1-10. (Intervento presentato al convegno 41st IEEE VLSI Test Symposium tenutosi a San Diego CA (USA) nel 24-26 April 2023) [10.1109/vts56346.2023.10139932].
Special Session: Neuromorphic hardware design and reliability from traditional CMOS to emerging technologies
Alessio Carpegna;Stefano Di Carlo;Alessandro Savino
2023
Abstract
The field of neuromorphic computing has been rapidly evolving in recent years, with an increasing focus on hardware design and reliability. This special session paper provides an overview of the recent developments in neuromorphic computing, focusing on hardware design and reliability. We first review the traditional CMOS-based approaches to neuromorphic hardware design and identify the challenges related to scalability, latency, and power consumption. We then investigate alternative approaches based on emerging technologies, specifically integrated photonics approaches within the NEUROPULS project. Finally, we examine the impact of device variability and aging on the reliability of neuromorphic hardware and present techniques for mitigating these effects. This review is intended to serve as a valuable resource for researchers and practitioners in neuromorphic computing.File | Dimensione | Formato | |
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https://hdl.handle.net/11583/2981394