Machine Learning
Support vector machines, reproducing kernel Hilbert spaces, and randomized GACV
Advances in kernel methods
Remembrance of circuits past: macromodeling by data mining in large analog design spaces
Proceedings of the 39th annual Design Automation Conference
Support vector machines for analog circuit performance representation
Proceedings of the 40th annual Design Automation Conference
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Proceedings of the 43rd annual Design Automation Conference
Considering Cost Asymmetry in Learning Classifiers
The Journal of Machine Learning Research
Statistical diagnosis of unmodeled systematic timing effects
Proceedings of the 45th annual Design Automation Conference
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Efficient SRAM failure rate prediction via Gibbs sampling
Proceedings of the 48th Design Automation Conference
Proceedings of the 50th Annual Design Automation Conference
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Leveraging machine learning has been proven as a promising avenue for addressing many practical circuit design and verification challenges. We demonstrate a novel active learning guided machine learning approach for characterizing circuit performance. When employed under the context of support vector machines, the proposed probabilistically weighted active learning approach is able to dramatically reduce the size of the training data, leading to significant reduction of the overall training cost. The proposed active learning approach is extended to the training of asymmetric support vector machine classifiers, which is further sped up by a global acceleration scheme. We demonstrate the excellent performance of the proposed techniques using three case studies: PLL lock-time verification, SRAM yield analysis and prediction of chip peak temperature using a limited number of on-chip temperature sensors.