Sustainable AI: Energy-Efficient Deep Learning Architectures for Edge Devices

Abstract : The rapid proliferation of artificial intelligence (AI) applications on edge devices has sparked significant interest in developing sustainable, energy-efficient deep learning architectures. Edge devices, such as smartphones, IoT sensors, and embedded systems, often operate under stringent power and computational constraints, making traditional resource-intensive deep learning models impractical for deployment. This paper explores recent advancements in designing energy-efficient deep learning architectures tailored for edge computing environments, aiming to balance model accuracy with reduced energy consumption and latency. We investigate techniques including model compression, quantization, pruning, and knowledge distillation that significantly lower the computational footprint without severely compromising performance. Furthermore, we examine novel lightweight architectures such as MobileNets, EfficientNets, and spiking neural networks designed explicitly for low-power operation. The integration of hardware-aware neural architecture search (NAS) is also discussed as an automated approach to optimize model design for specific edge platforms. Experimental evaluations on benchmark datasets demonstrate that these sustainable AI models achieve comparable accuracy to conventional deep networks while reducing energy usage by up to 70%. This energy efficiency not only extends battery life but also contributes to reducing the carbon footprint associated with AI deployment at scale. Finally, we address challenges related to real-time processing, hardware heterogeneity, and privacy concerns in edge AI systems. The insights and methodologies presented in this work provide a foundation for future research and practical implementations of sustainable AI, facilitating widespread adoption of intelligent edge devices that are both performant and environmentally responsible.

Keyword Sustainable AI, Energy-Efficient Deep Learning, Edge Devices, Model Compression, Quantization, Pruning, Knowledge Distillation, Neural Architecture Search, MobileNets, EfficientNets, Spiking Neural Networks, Edge Computing, Low-Power AI, Environmental Impact.