NanoHydra: Energy-Efficient Time-Series Classification at the Edge
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917035760418816 |
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| author | Cioflan, Cristian Fonseca, Jose Wang, Xiaying Benini, Luca |
| author_facet | Cioflan, Cristian Fonseca, Jose Wang, Xiaying Benini, Luca |
| contents | Time series classification (TSC) on extreme edge devices represents a stepping stone towards intelligent sensor nodes that preserve user privacy and offer real-time predictions. Resource-constrained devices require efficient TinyML algorithms that prolong the device lifetime of battery-operated devices without compromising the classification accuracy. We introduce NanoHydra, a TinyML TSC methodology relying on lightweight binary random convolutional kernels to extract meaningful features from data streams. We demonstrate our system on the ultra-low-power GAP9 microcontroller, exploiting its eight-core cluster for the parallel execution of computationally intensive tasks. We achieve a classification accuracy of up to 94.47% on ECG5000 dataset, comparable with state-of-the-art works. Our efficient NanoHydra requires only 0.33 ms to accurately classify a 1-second long ECG signal. With a modest energy consumption of 7.69 uJ per inference, 18x more efficient than the state-of-the-art, NanoHydra is suitable for smart wearable devices, enabling a device lifetime of over four years. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_20038 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NanoHydra: Energy-Efficient Time-Series Classification at the Edge Cioflan, Cristian Fonseca, Jose Wang, Xiaying Benini, Luca Signal Processing Time series classification (TSC) on extreme edge devices represents a stepping stone towards intelligent sensor nodes that preserve user privacy and offer real-time predictions. Resource-constrained devices require efficient TinyML algorithms that prolong the device lifetime of battery-operated devices without compromising the classification accuracy. We introduce NanoHydra, a TinyML TSC methodology relying on lightweight binary random convolutional kernels to extract meaningful features from data streams. We demonstrate our system on the ultra-low-power GAP9 microcontroller, exploiting its eight-core cluster for the parallel execution of computationally intensive tasks. We achieve a classification accuracy of up to 94.47% on ECG5000 dataset, comparable with state-of-the-art works. Our efficient NanoHydra requires only 0.33 ms to accurately classify a 1-second long ECG signal. With a modest energy consumption of 7.69 uJ per inference, 18x more efficient than the state-of-the-art, NanoHydra is suitable for smart wearable devices, enabling a device lifetime of over four years. |
| title | NanoHydra: Energy-Efficient Time-Series Classification at the Edge |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2510.20038 |