Edge computing and sustainable, low-power AI systems

Abstract

The increasing use of artificial intelligence (AI) in smart environments, industrial automation, healthcare monitoring, and Internet of Things (IoT) networks has increased the computational requirements and power consumption of cloud computing infrastructure. The use of cloud computing for AI processing faces challenges such as high latency, high bandwidth consumption, privacy concerns, and environmental emissions due to the large number of data centers.
This work introduces a sustainable and low-power AI model based on the smart edge architecture․ It includes energy-efficient machine learning algorithms

deployed at the edge for making smart decisions․ This model achieves optimal performance using lightweight model deployment, dynamic resource allocation, and hardware-aware optimizations such as model quantization and hardware pruning techniques․ A modular architecture is proposed that increases efficiency by focusing on data acquisition, edge processing, AI inference, energy management, and cloud synchronization․
Performance metrics include latency, energy consumption, compute efficiency, and inference quality․ The latter, in particular, is important for providing efficient AI capabilities on devices with limited resources․ Results of experiments show that local processing reduces network

and energy use compared to processing in the cloud․ The proposed framework enables scalable and efficient deployment of AI while minimizing the environmental impact and maintaining performance to support sustainable computing․