Runtime adjustment of IoT system-on-chips for minimum energy operation

Energy-constrained Systems-on-Chips (SoC) are becoming major components of many emerging applications, especially in the Internet of Things (IoT) domain. Although the best energy efficiency is achieved when the SoC operates in the near-threshold region, the best operating point for maximum energy efficiency could vary depending on operating temperature, workload, and the power-gating state (power modes) of various SoC components at runtime. This paper presents a lightweight machine-learning based scheme to predict and tune the SoC to the most energy efficient supply voltage at the firmware level during runtime, considering the impacts of temperature variation and power-gating of SoC components while meeting the performance and reliability requirements. Simulation results indicate that the proposed method can determine the most energy efficient supply voltage of a circuit with high-accuracy (RMSE = 7mV), while considering the runtime performance and reliability constraints.