AI and Machine Learning Enhancements in Embedded Systems: A Scholarly Analysis
Sanjeev Shankar · European Modern Studies Journal · 2025
This article examines the transformative impact of artificial intelligence and machine learning on embedded systems development. The integration of AI capabilities into firmware engineering represents a paradigm shift that addresses critical challenges in performance, efficiency, and security within resource-constrained environments. From automated code optimization and predictive maintenance to intelligent resource management and enhanced security protocols, AI technologies are enabling unprecedented capabilities in embedded devices. The article explores how on-device machine learning frameworks facilitate edge computing without cloud dependency, while distributed intelligence approaches optimize workload distribution across IoT networks. Additionally, it highlights how AI-driven security mechanisms establish comprehensive protection through anomaly detection, adaptive security measures, and enhanced firmware verification. Through these advancements, embedded systems are achieving new levels of functionality, efficiency, and security that were previously unattainable with traditional development approaches.