Learning of Multi-Dimensional Analog Circuits Through Generative Adversarial Network (GAN)
| Autor: | Vijay Chandrasekhar, Balagopal Unnikrishnan, Yong-Joon Jeon, Ashish James, Zeng Zeng, Rahul Dutta, Chemmanda John Leo, Kevin T. C. Chai, Chuan-Sheng Foo, Salahuddin Raju |
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| Rok vydání: | 2019 |
| Předmět: |
Analogue electronics
business.industry Computer science Supervised learning 02 engineering and technology Semi-supervised learning Variance (accounting) Space (commercial competition) Machine learning computer.software_genre 0202 electrical engineering electronic engineering information engineering Multi dimensional Labeled data 020201 artificial intelligence & image processing Artificial intelligence business computer Generative adversarial network |
| Zdroj: | SoCC |
| Popis: | Analog circuits are strictly designed under operational, functional and technology constraints. Together, these bounds create a sparse multi-dimensional design optimization space with the scarcity of labeled analog training data making supervised learning methods ineffective. Accurate approximation of multi-target analog circuits, therefore, requires generation of labeled data around dominant bias and with relevant variance. With such an approach, we explore state-of-the-art semi-supervised, generative adversarial network (GAN) towards analog performance modeling. We report on various multi-target analog circuit classification experiments and demonstrate stable GAN performance achieving 2-5% higher accuracy and utilizing only 10% fully simulated manually annotated labeled data against supervised learning methods. |
| Databáze: | OpenAIRE |
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