APA
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Kumar, R., Narayan, S., Kumar, S., Roy, S., Kaushik, B. K., Achar, R., & Sharma, R. (2021). Knowledge-Based Neural Networks for Fast Design Space Exploration of Hybrid Copper-Graphene On-Chip Interconnect Networks. IEEE Transactions on Electromagnetic Compatibility (Print).
Chicago/Turabian
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Kumar, Rahul, S. Narayan, Somesh Kumar, Sourajeet Roy, Brajesh Kumar Kaushik, R. Achar, and Rohit Sharma. “Knowledge-Based Neural Networks for Fast Design Space Exploration of Hybrid Copper-Graphene On-Chip Interconnect Networks.” IEEE transactions on electromagnetic compatibility (Print) (2021).
MLA
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Kumar, Rahul, et al. “Knowledge-Based Neural Networks for Fast Design Space Exploration of Hybrid Copper-Graphene On-Chip Interconnect Networks.” IEEE Transactions on Electromagnetic Compatibility (Print), 2021.
BibTeX Click to copy
@article{rahul2021a,
title = {Knowledge-Based Neural Networks for Fast Design Space Exploration of Hybrid Copper-Graphene On-Chip Interconnect Networks},
year = {2021},
journal = {IEEE transactions on electromagnetic compatibility (Print)},
author = {Kumar, Rahul and Narayan, S. and Kumar, Somesh and Roy, Sourajeet and Kaushik, Brajesh Kumar and Achar, R. and Sharma, Rohit}
}
In this article, an artificial neural network (ANN) is developed in order to predict the per-unit-length (p. u. l.) parameters of hybrid copper-graphene on-chip interconnects from a prior knowledge of their structural geometry and layout. The salient feature of the proposed ANN is that it combines knowledge of the p. u. l. parameters extracted from empirical models along with that extracted from a rigorous full-wave electromagnetic solver. As a result, the proposed ANN is referred to as a knowledge-based neural network (KBNN). The KBNN has been found to converge to the same accuracy as a conventional ANN but at the expense of far smaller training time costs. As a result, the KBNN is much more suitable for performing design space explorations.