Multiperspective and Energy-Efficient Deep Learning in Edge Computing

The deployment of billions of Internet of Things (IoT) devices is driving unprecedented data generation at the network edge, demanding high computational power for real-time deep learning (DL) while raising serious concerns about energy consumption. While edge computing offers a viable paradigm for decentralized DL by preserving data privacy and reducing latency, the substantial energy costs of DL training and inference pose a major challenge for resource-constrained edge devices. This work provides a comprehensive review of state-of-the-art studies that address energy efficiency at the intersection of DL and edge computing. Moving beyond isolated solutions, we analyze the critical need for a codesign approach integrating hardware and software with adaptive resource management to build sustainable systems. The article systematically examines hardware-level optimizations and software-level techniques for reducing energy consumption while maintaining model accuracy. Furthermore, it investigates how adaptive management of compute, memory, and communication resources is key to dynamic energy savings. Finally, the article synthesizes recent trends, identifies emerging opportunities, and discusses open challenges, positioning hardware–software codesign as the most promising approach for achieving scalable and energy-efficient DL in edge computing.