Domain-Specific Machine Learning Based Minimum Operating Voltage Prediction Using On-Chip Monitor Data.

Determining the minimum operating voltage ( $V_{min}$ ) of chip designs is critical for low power dissipation and assurance of quality and functional safety during manufacturing tests and in-field monitoring. We demonstrate how on-chip monitor data can be leveraged to provide accurate minimum operating voltage prediction using a domain-specific machine learning approach. Given limited measured chip data, the key challenge in developing a machine learning approach is to provide an accurate prediction while addressing overfitting and selecting a subset of optimal features. To this end, we propose to utilize a novel monotonic lattice neural network architecture that is geared towards accurate prediction by imposing domain-specific monotonic relationships between the input sensor data and $V_{min}$ . Furthermore, we perform an effective feature selection by considering both the correlation between each feature and $V_{min}$ as well as the co-linearity between the features. Experiments demonstrate superior performance in comparison with linear regression and conventional neural networks.