부산대학교 도서관

In safety-critical applications, microcontrollers have to be tested to satisfy strict quality and performance constraints. It has been demonstrated that on-chip ring oscillators can be used as speed monitors to reliably predict the performances. However, any machine-learning (ML) model is likely to be inaccurate if trained on an inadequate dataset, and labeling data for training is quite a costly process. In this article, we present a methodology based on active learning to select the best samples to be included in the training set, significantly reducing the time and cost required. Moreover, since different speed measurements are available, we designed a multilabel technique to take advantage of their correlations. Experimental results demonstrate that the approach halves the training-set size, with respect to a random-labeling, while it increases the predictive accuracy, with respect to standard single-label ML models.