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@article{189023,
        author = {Pranali Sureshrao Mandale},
        title = {Energy-Efficient AI Models(TinyML) For Edge Devices},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {4482-4482},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189023},
        abstract = {Tiny Machine Learning (TinyML) focuses on deploying machine learning models on highly resource-constrained edge devices such as microcontrollers and low-power systems-on-chip. This paper surveys efficient AI techniques for edge deployment and proposes a co-design framework combining quantization, pruning, knowledge distillation, neural architecture search, and microcontroller-aware optimization. The study highlights how TinyML enables low-latency, energy-efficient, and privacy-preserving intelligence at the edge.},
        keywords = {TinyML, Edge AI, Model Compression, Quantization, Microcontrollers},
        month = {December},
        }