Research

The need for faster Systems on Chip (SoCs) has accelerated scaling trends, leading to a considerable power density increase and raising critical power and thermal challenges. The ability to measure power consumption of different hardware units is essential for the operation and improvement of mobile SoCs, as well as the enhancement of the power efficiency of the software that runs on them. SoCs are usually enabled with embedded thermal sensors to measure the temperature at the hardware unit level; however, they lack the ability to sense the power. In this paper we introduce an Alternating Blind Identification of Power sources (Alternating-BPI), a technique that accurately estimates the power consumption of individual SoC units without the use of any design based models. The proposed technique uses a novel approach to blindly identify the sources of power consumption, by relying only on the measurements from the embedded thermal sensors and the total power consumption. The accuracy and applicability of the proposed technique was verified using simulation and experimental data. Alternating-BPI is able to estimate the power at the SoC hardware unit level with up to 98.1% accuracy. Furthermore, we demonstrate the applicability of the proposed technique on a commercial SoC and provide a fine-grain analysis of the power profiles of CPU and GPU Apps, as well as Artificial Intelligence (AI), Virtual Reality (VR) and Augmented Reality (AR) Apps. Additionally, we demonstrate that the proposed technique could be used to estimate the power consumption per-process by relying on the estimated per-unit power numbers and per-unit hardware utilization numbers. The analysis provided by the proposed technique gives useful insights about the power efficiency of the different hardware units on a state-of-the-art commercial SoC.