Deep Learning Autoencoder-based Compression for Current Source Model Waveforms

Author:

Raslan Waseem1,Ismail Yehea2

Affiliation:

1. Siemens EDA, Siemens Digital Industries Software,IC Verification Solutions,Cairo,Egypt

2. The American University in Cairo,Center of Nanoelectronics and Devices, CND,Cairo,Egypt

Publisher

IEEE

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Cell Library Characterization for Composite Current Source Models Based on Gaussian Process Regression and Active Learning;Proceedings of the 2024 ACM/IEEE International Symposium on Machine Learning for CAD;2024-09-09

2. ANN-based Standard Cell Switching Waveforms Characterization Model for PVT Variations;2024 4th International Conference on Neural Networks, Information and Communication (NNICE);2024-01-19

3. A Novel Side-Channel Archive Framework Using Deep Learning-Based Leakage Compression;IEEE Access;2024

4. Deep-learning cell-delay modeling for static timing analysis;Ain Shams Engineering Journal;2023-02

5. Survey of Machine Learning for Electronic Design Automation;Proceedings of the Great Lakes Symposium on VLSI 2022;2022-06-06

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