Architectural Overview of Edge AI Processing in Smart Connected Devices: From Embedded Hardware to Real-Time Inference

Abstract

Edge artificial intelligence represents a paradigm shift in computing architecture, enabling machine learning inference directly on embedded devices rather than relying on cloud infrastructure. This article provides a comprehensive examination of edge AI systems, exploring the technical foundations, optimization techniques, and real-world implementations that make local intelligence processing feasible within the constraints of consumer hardware. The analysis covers processor selection criteria, including neural processing units, tensor processing units, and digital signal processors, alongside memory hierarchy optimization and power management strategies essential for resource-constrained environments. Model optimization techniques such as quantization, pruning, knowledge distillation, and dynamic inference are examined to demonstrate how sophisticated AI capabilities can be compressed and deployed on edge devices. Through case studies in voice processing, anomaly detection, and computer vision, the paper illustrates practical implementations and their performance characteristics. The discussion extends to emerging hardware technologies, standardization efforts, privacy implications, and research challenges in federated learning that will shape the future of edge AI. This comprehensive overview provides engineers and researchers with insights into designing efficient embedded systems capable of running AI models locally, thereby enabling faster response times, enhanced privacy, and reduced network dependencies in smart connected devices.