Print Email Facebook Twitter Analog monolayer SWCNTs-based memristive 2D structure for energy-efficient deep learning in spiking neural networks Title Analog monolayer SWCNTs-based memristive 2D structure for energy-efficient deep learning in spiking neural networks Author Abunahla, H.N. (TU Delft Computer Engineering) Abbas, Yawar (Khalifa University) Gebregiorgis, A.B. (TU Delft Computer Engineering) Waheed, Waqas (Khalifa University of Science and Technology) Mohammad, Baker (Khalifa University) Hamdioui, S. (TU Delft Computer Engineering) Alazzam, Anas (Khalifa University) Rezeq, Moh’d (Khalifa University) Date 2023 Abstract Advances in materials science and memory devices work in tandem for the evolution of Artificial Intelligence systems. Energy-efficient computation is the ultimate goal of emerging memristor technology, in which the storage and computation can be done in the same memory crossbar. In this work, an analog memristor device is fabricated utilizing the unique characteristics of single-wall carbon nanotubes (SWCNTs) to act as the switching medium of the device. Via the planar structure, the memristor device exhibits analog switching ability with high state stability. The device’s conductance and capacitance can be tuned simultaneously, increasing the device's potential and broadening its applications' horizons. The multi-state storage capability and long-term memory are the key factors that make the device a promising candidate for bio-inspired computing applications. As a demonstrator, the fabricated memristor is deployed in spiking neural networks (SNN) to exploit its analog switching feature for energy-efficient classification operation. Results reveal that the computation-in-memory implementation performs Vector Matrix Multiplication with 95% inference accuracy and few femtojoules per spike energy efficiency. The memristor device presented in this work opens new insights towards utilizing the outstanding features of SWCNTs for efficient analog computation in deep learning systems. Subject OA-Fund TU Delft To reference this document use: http://resolver.tudelft.nl/uuid:f0c6706d-818e-4760-aaff-02e07473264d DOI https://doi.org/10.1038/s41598-023-48529-z ISSN 2045-2322 Source Scientific Reports, 13 (1) Part of collection Institutional Repository Document type journal article Rights © 2023 H.N. Abunahla, Yawar Abbas, A.B. Gebregiorgis, Waqas Waheed, Baker Mohammad, S. Hamdioui, Anas Alazzam, Moh’d Rezeq Files PDF s41598_023_48529_z.pdf 2.66 MB Close viewer /islandora/object/uuid:f0c6706d-818e-4760-aaff-02e07473264d/datastream/OBJ/view