Noise-Aware Uncertainty Quantification of MLGNR Interconnects using Fast Trained Artificial Neural Networks
Author:
Affiliation:
1. Indian Institute of Technology Roorkee,Department of Electronics and Communication Engineering,Roorkee,India
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10467193/10468066/10468166.pdf?arnumber=10468166
Reference5 articles.
1. A comparative scaling analysis of metallic and carbon nanotube interconnections for nanometer scale VLSI technologies;Srivastava
2. Modified Knowledge-Based Neural Networks Using Control Variates for the Fast Uncertainty Quantification of On-Chip MWCNT Interconnects
3. Exploring the Impact of Parametric Variability on Eye Diagram of On-Chip Multi-walled Carbon Nanotube Interconnects using Fast Machine Learning Techniques
4. A Transfer Learning Approach to Expedite Training of Artificial Neural Networks for Variability-Aware Signal Integrity Analysis of MWCNT Interconnects
5. Prior Knowledge Accelerated Transfer Learning (PKI-TL) for Machine Learning Assisted Uncertainty Quantification of MLGNR Interconnect Networks
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