nanoML for Human Activity Recognition
Fuente:
arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866912235608080384 |
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| author | Bacellar, Alan T. L. Jadhao, Mugdha P. Nag, Shashank Lima, Priscila M. V. Franca, Felipe M. G. John, Lizy K. |
| author_facet | Bacellar, Alan T. L. Jadhao, Mugdha P. Nag, Shashank Lima, Priscila M. V. Franca, Felipe M. G. John, Lizy K. |
| contents | Human Activity Recognition (HAR) is critical for applications in healthcare, fitness, and IoT, but deploying accurate models on resource-constrained devices remains challenging due to high energy and memory demands. This paper demonstrates the application of Differentiable Weightless Neural Networks (DWNs) to HAR, achieving competitive accuracies of 96.34% and 96.67% while consuming only 56nJ and 104nJ per sample, with an inference time of just 5ns per sample. The DWNs were implemented and evaluated on an FPGA, showcasing their practical feasibility for energy-efficient hardware deployment. DWNs achieve up to 926,000x energy savings and 260x memory reduction compared to state-of-the-art deep learning methods. These results position DWNs as a nano-machine learning nanoML model for HAR, setting a new benchmark in energy efficiency and compactness for edge and wearable devices, paving the way for ultra-efficient edge AI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_12173 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | nanoML for Human Activity Recognition Bacellar, Alan T. L. Jadhao, Mugdha P. Nag, Shashank Lima, Priscila M. V. Franca, Felipe M. G. John, Lizy K. Machine Learning Artificial Intelligence Human Activity Recognition (HAR) is critical for applications in healthcare, fitness, and IoT, but deploying accurate models on resource-constrained devices remains challenging due to high energy and memory demands. This paper demonstrates the application of Differentiable Weightless Neural Networks (DWNs) to HAR, achieving competitive accuracies of 96.34% and 96.67% while consuming only 56nJ and 104nJ per sample, with an inference time of just 5ns per sample. The DWNs were implemented and evaluated on an FPGA, showcasing their practical feasibility for energy-efficient hardware deployment. DWNs achieve up to 926,000x energy savings and 260x memory reduction compared to state-of-the-art deep learning methods. These results position DWNs as a nano-machine learning nanoML model for HAR, setting a new benchmark in energy efficiency and compactness for edge and wearable devices, paving the way for ultra-efficient edge AI. |
| title | nanoML for Human Activity Recognition |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2502.12173 |