Abstract

The integration of artificial intelligence (AI) into consumer electronics has redefined user experiences by enabling smart functionalities in devices such as smartphones, wearables, and smart home systems. However, delivering AI capabilities on compact, battery-operated devices presents a major engineering challenge: achieving high computational performance within strict power and thermal constraints. Systems-on-Chip (SoCs) have emerged as the hardware foundation for enabling efficient AI processing at the edge, offering tightly integrated components optimized for performance-per-watt.
This paper explores design methodologies for developing power-efficient SoCs tailored for AI-driven consumer electronics. We focus on architectural strategies that balance processing throughput with minimal energy usage, including dynamic voltage and frequency scaling, heterogeneous multicore designs, memory subsystem optimization, and the integration of domain-specific AI accelerators such as neural processing units (NPUs) and tensor cores. Additionally, compute-in-memory (CIM) techniques are analyzed as solutions to the energy bottleneck caused by data movement.
Our analysis draws from recent academic research and industrial implementations up to December 2024, highlighting power-performance trade-offs across various SoC platforms. Through case studies of commercial AI SoCs—such as Apple’s A-series and Google’s Tensor—we assess techniques like power gating, software-hardware co-design, and runtime energy-aware scheduling that contribute to reduced power consumption.
The paper concludes that energy efficiency in AI SoCs demands a holistic co-design approach, integrating innovations across hardware architecture, compiler optimization, and AI model design. We also discuss emerging trends including chiplet architectures, 3D integration, and neuromorphic designs that promise further gains in energy efficiency. This study aims to guide future efforts in building intelligent, energy-conscious consumer electronics.