{"ID":2851940,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.20038","arxiv_id":"2510.20038","title":"NanoHydra: Energy-Efficient Time-Series Classification at the Edge","abstract":"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.","short_abstract":"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 compromi...","url_abs":"https://arxiv.org/abs/2510.20038","url_pdf":"https://arxiv.org/pdf/2510.20038v1","authors":"[\"Cristian Cioflan\",\"Jose Fonseca\",\"Xiaying Wang\",\"Luca Benini\"]","published":"2025-10-22T21:31:17Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[]","has_code":false}