Intelligent Embedded Systems for Real-Time Applications

Laurent Pautet

Intelligent Embedded Systems for Real-Time Applications

Author(s)	Laurent Pautet
Country	France
Abstract	Intelligent embedded systems have become an important technological foundation for real-time applications requiring rapid computation, autonomous decision-making, low latency, and reliable operation. The integration of embedded computing with artificial intelligence, machine learning, Internet of Things technologies, edge computing, and real-time operating systems has enabled intelligent functionality to move closer to the point of data generation. This paper examines intelligent embedded systems for real-time applications through a conceptual and literature-based research approach. The study focuses on embedded processors, sensors, real-time operating systems, edge intelligence, TinyML, hardware acceleration, real-time scheduling, communication, and intelligent decision-making. The analysis indicates that intelligent embedded systems can improve responsiveness by processing information locally and reducing dependence on centralized computing infrastructure. Recent research also demonstrates increasing interest in deploying machine learning models on resource-constrained embedded devices, where latency, energy efficiency, memory limitations, and computational capacity must be carefully balanced. However, implementation remains constrained by limited resources, thermal and energy considerations, model complexity, security, reliability, scheduling, and system validation. The study highlights the importance of hardware-software co-design, lightweight artificial intelligence, efficient scheduling, edge processing, and dependable real-time architectures. Future research should focus on adaptive embedded intelligence, on-device learning, efficient AI accelerators, explainable edge AI, secure architectures, and intelligent resource management. The study concludes that intelligent embedded systems can provide an effective technological foundation for responsive and autonomous real-time applications when computational intelligence is carefully integrated with deterministic system requirements.
Keywords	Intelligent Embedded Systems, Real-Time Applications, Embedded Computing, Edge AI, TinyML, Real-Time Systems, Artificial Intelligence, Machine Learning, Hardware Acceleration, Intelligent Computing.
Field	Engineering
Published In	Volume 4, Issue 5, September-October 2022
Published On	2022-10-16

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American International Journal of Advanced Research and Development

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Intelligent Embedded Systems for Real-Time Applications

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