Circuit Performance Classification With Active Learning Guided Sampling for Support Vector Machines

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 (SVMs), 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 SVM classifiers, which is further sped up by a global acceleration scheme. We demonstrate the excellent performance of the proposed techniques using four case studies: 1) dc/dc converter ripple noise analysis; 2) phase-locked loop lock-time verification; 3) reliability analysis of a ring oscillator with respect to process variations and initial conditions; and 4) prediction of chip peak temperature using a limited number of on-chip temperature sensors.