Date of Publication :7th September 2025

Abstract: Embedded systems, specialized computer systems integrated into larger mechanical or electrical frameworks, are pivotal in controlling a wide array of modern devices, characterized by low power consumption, compact size, and real-time computing constraints. These systems, predominantly microcontroller-based, face programming challenges due to limited processing resources but can leverage intelligent mechanisms to optimize resource management and enhance functionalities like power efficiency. The integration of Artificial Intelligence (AI) into embedded systems has revolutionized applications across diverse sectors, including automotive, agriculture, education, and cybersecurity, driven by advancements in AI algorithms, neural network compression, and hardware accelerators. In agriculture, embedded AI facilitates real-time crop monitoring and yield estimation, overcoming climate and resource challenges through IoT and low-power sensing systems. In education, lightweight AI models on embedded devices enable real-time emotion recognition and interactive teaching platforms, enhancing classroom engagement. The automotive industry has seen a paradigm shift with embedded systems enhancing vehicle intelligence, safety, and connectivity, particularly in electric and autonomous vehicles, though challenges like cybersecurity and standardization persist. Cybersecurity in embedded systems benefits from AI-driven intrusion detection and malware prevention, addressing escalating threats in interconnected environments. Hardware-software co-design approaches, such as programmable parsers and extended RISC microprocessors, improve performance and support hybrid applications, while Bayesian Networks optimize AI for resource-constrained autonomous driving. Vision pipelines in cyber-physical systems leverage near-sensor and in-sensor processing to reduce latency and power consumption, critical for autonomous vehicles and mobile devices. Despite their potential, embedded AI systems face design challenges, including resource constraints, real-time requirements, and privacy risks, necessitating innovative modelling techniques and formal methods. This review synthesizes the applications, challenges, and future prospects of embedded systems and AI, highlighting their transformative impact across industries and the need for scalable, secure, and efficient solutions.

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