INTEGRATING AI MODELS INTO LOW-POWER MICROCONTROLLERS FOR EDGE COMPUTING
Keywords:
Internet of Things (IoT), Artificial Intelligence (AI) models, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs)Abstract
The proliferation of the Internet of Things (IoT) drives a demand for intelligent data processingat the network's edge. Traditional Artificial Intelligence (AI) models, optimized for high-performance cloud systems, are incompatible with the limited computational, memory, and energy resources of low-power microcontrollers
References
Redmon, J., & Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv preprint arXiv:1804.02767.
Kiran, B. R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A., Yogamani, S., & Pérez, P. (2021). Deep Reinforcement Learning for Autonomous Driving: A Survey. IEEE Transactions on Intelligent Transportation Systems, 22(6), 1–18.
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Published
2024-05-15
How to Cite
Manish Khute, Dr. Vijayalaxmi biradar. (2024). INTEGRATING AI MODELS INTO LOW-POWER MICROCONTROLLERS FOR EDGE COMPUTING. Journal of Computational Analysis and Applications (JoCAAA), 33(05), 3703–3710. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5276
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