Artificial Intelligence-driven Optimization of Electronic Device Performance and Energy Efficiency

Abstract

Artificial intelligence (AI) is transforming the energy efficiency landscape of modern electronic systems by enabling intelligent optimization across algorithms, hardware architectures, and mixed‑signal interfaces. Through predictive workload management, adaptive task scheduling, and reinforcement‑learning‑based control policies, AI reduces redundant computation and data movement, which are the two dominant contributors to power consumption in mobile, IoT, and embedded platforms. Complementary algorithmic advances, including model compression and lightweight neural architectures, further minimize resource usage while preserving performance. At the hardware level, innovations such as edge AI accelerators, memory‑centric and compute‑in‑memory architectures, and adaptive memristor‑based sensing pipelines improve performance per watt by tightly integrating computation with storage and signal acquisition. System‑level intelligence additionally enables coordinated energy governance in distributed electronics, supporting efficient load balancing, renewable‑energy interaction, and improved responsiveness in smart‑grid and cyber‑physical environments. Despite these advances, critical challenges persist, including reproducible energy‑measurement standards, privacy‑preserving telemetry for device analytics, robust verification of mixed‑signal AI circuits, and the absence of unified cross‑layer co‑design methodologies. Addressing these gaps is essential for the development of next‑generation electronic systems capable of delivering high‑performance intelligence while ensuring sustainable, energy‑responsible operation.