期刊
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
卷 42, 期 10, 页码 3436-3449出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCAD.2023.3245989
关键词
Active learning (AL); device testing; Fmax; machine learning (ML); performance screening; speed monitors (SMONs)
This article presents a methodology based on active learning to select the best samples for testing microcontrollers. By using a multilabel technique to improve predictive accuracy, the approach reduces the training set size and the time and cost required for training.
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.
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