Enabling Inter-Product Transfer Learning on MCU Performance Screening

In safety-critical applications, microcontrollers must meet strict quality and performance standards, including the maximum operating frequency (Fmax). Machine learning (ML) models can estimate Fmax using data from on-chip ring oscillators (ROs), making them suitable for performance screening. However, when new products are introduced, existing ML models may no longer be suitable and require updating. Training a new model from scratch is challenging due to limited data availability. Acquiring Fmax data is time-consuming and costly, resulting in a small labeled dataset. However, a large amount of data from legacy products may be available, along with existing ML models. In order to address the scarcity of labeled data, this paper proposes using deep learning feature extractors trained on specific MCU product data and fine-tuning them for new devices, in a Transfer Learning fashion. Experimental results show that these models can extract useful general features for performance prediction. As a result, they achieve better performance with significantly less labeled data compared to traditional shallow learning approaches.