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@article{174969,
        author = {Bharatha B and Chandana B N and Chirag P I and Deepanjali and Govinda Raju M},
        title = {A Survey on The Latest Techniques for Implementing Machine Learning and Artificial Intelligence Algorithms on Resource-constrained Embedded Devices},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {1249-1256},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174969},
        abstract = {This comprehensive examination explores the integration of AI into embedded systems with limited resources, with a focus on the development of AI models and algorithms. It examines neural network compression, hardware acceleration techniques, and modern application paradigms to illustrate the challenges of deploying intelligent technology in constrained environments. By reviewing relevant literature to elucidate the benefits and drawbacks of existing approaches and to guide the discussion toward possible future developments, the study emphasizes the crucial role that embedded AI plays in fostering innovation, efficiency, and compatibility within the Internet of Things. Given the increasing integration of machine learning models into computationally limited devices, this study investigates the platforms, optimizations, and techniques for embedding these models into low-resource microcontroller units (MCUs). The research aims to assist the decentralization of network intelligence while emphasizing embedded AI’s ability to increase productivity and accelerate digital transformation by offering concepts, taxonomies, principles, and future directions.},
        keywords = {constrained, Machine Learning, Artificial Intelligence, Embedded System},
        month = {April},
        }