DESIGN STRATEGIES FOR MOBILE SOCS: BALANCING BATTERY LIFE AND COMPUTATIONAL DEMAND

Abstract

Modern smartphones are based on highly integrated Mobile System-on-Chip (SoC) technologies enabling functionalities in communication, high-performance gaming, and many others. As there is an increasing demand of high performance and energy efficiency in mobile electronics, design of System-on-Chips (SoCs) becomes more important. The battery life is still one of the key factors affecting the usability and life cycle of the mobile devices. System-on-Chip (SoC) technologies have facilitated the action of high-performance computing abilities in small and energy-constrained platforms. With the increase in real-time applications like augmented reality (AR), artificial intelligence (AI), and high-resolution multimedia, there comes a challenge of balancing between energy utilization and computational power, which is a major challenge to design. In this review, there is a detailed discussion of the modern mobile System-on-Chip (SoC) creations, energy-saving hardware solutions, advanced battery management system (BMS) approaches, and software-level optimization solutions. The role of heterogeneous computing and machine learning (ML) in dynamic workload management and energy optimization is of special concern. The possibility of artificial intelligence-based battery management system (AI-based BMS) to increase efficiency of operation and health of the battery is also discussed. In addition, Mobile Edge Computing (MEC) structures permitting venture in unmanned aerial vehicle (UAV)-based frameworks are examined and broadly contrasted qualitatively regarding algorithmic strategies. The paper ends with section devoted to the discussion of the current issues and the future research avenues in the design of energy-efficient mobile System-on-Chip (SoC) systems, that offer high performance.