Machine Learning for Noise Sensor Placement and Full-Chip Voltage Emergency Detection

Abstract

Power supply fluctuation can be potential threat to the correct operations of processors, in the form of voltage emergency that happens when supply voltage drops below a certain threshold. Noise sensors (with either analog or digital outputs) can be placed in the nonfunction area of processors to detect voltage emergencies by monitoring the runtime voltage fluctuations. Our work addresses two important problems related to building a sensor-based voltage emergency detection system: 1) offline sensor placement, i.e., where to place the noise sensors so that the number and locations of sensors are optimized in order to strike a balance between design cost and chip reliability and 2) online voltage emergency detection, i.e., how to use these placed sensors to detect voltage emergencies in the hotspot locations. In this paper, we propose integrated solutions to these two problems, respectively, for analog and digital (more specifically, binary) sensor outputs, by exploiting the voltage correlation between the sensor candidate locations and the hotspot locations. For the analog case, we use the Group Lasso and an ordinary least squares approach; for the binary case, we integrate the Group Lasso and the SVM approach. Experimental results show that, our approach can achieve 2.3X-2.7X better voltage emergency detection results on average for analog outputs when compared to the state-of-the-art work; and for the binary case, on average our methodology can achieve up to 21% improvement in prediction accuracy compared to an approach called max-probability-no-prediction.