Design of Multi-Output Switched-Capacitor Voltage Regulator via Machine Learning
Zhiyuan Zhou, Syrine Belakaria, Aryan Deshwal, Wookpyo Hong, Janardhan Rao Doppa, Partha Pratim Pande and Deukhyoun Heo
School of EECS, Washington State University, Pullman, WA, U.S.A.
ABSTRACT
Efficiency of power management system (PMS) is one of the key performance metrics for highly integrated system on chips (SoCs). Towards the goal of improving power efficiency of SoCs, we make two key technical contributions in this paper. First, we develop a multi-output switched-capacitor voltage regulator (SCVR) with a new flying capacitor crossing technique (FCCT) and cloud-capacitor method. Second, to optimize the design parameters of SCVR, we introduce a novel machinelearning (ML)-inspired optimization framework to reduce the number of expensive design simulations. Simulation shows that power loss of the multi-output SCVR with FCCT is reduced by more than 40% compared to conventional multiple single-output SCVRs. Our ML-based design optimization framework is able to achieve more than 90% reduction in the number of simulations needed to uncover optimized circuit parameters of the proposed SCVR.
Keywords: Power management system, Electronic design automation, Machine Learning, Multi-objective Optimization.
