SparrowSNN: A Hardware/software Co-design for Energy Efficient ECG Classification
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arXiv
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| Format: | Preprint |
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2024
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| author | Yan, Zhanglu Bai, Zhenyu Mitra, Tulika Wong, Weng-Fai |
| author_facet | Yan, Zhanglu Bai, Zhenyu Mitra, Tulika Wong, Weng-Fai |
| contents | Deep learning has driven significant technological advancements, but its high energy consumption limits its use on battery-operated edge devices. Spiking Neural Networks (SNNs) offer promising reductions in inference-time energy consumption. However, existing neuromorphic architectures optimize scalable, many-core NoC execution, suited to large models but mismatched to edge devices, and their prevalent integrate-and-fire neurons re-read weights across \(T\) timesteps, inflating data-movement and dynamic-control energy. To address this challenge, we propose SparrowSNN, an optimized end-to-end design tailored for edge applications. SparrowSNN proposes: (1) a hardware-friendly spike activation function SSF (Sum-Spike-and-Fire); (2) a customizable $μ$W-level-power quantized hybrid ANN-SNN model that can be designed per application; (3) a compact and low-power reconfigurable ASIC architecture, supporting the aforementioned designs. Evaluated on biomedical MIT-BIH ECG and DEAP EEG datasets, SparrowSNN achieves state-of-the-art accuracy with $20\times$ to $100\times$ lower energy consumption, significantly outperforming existing ultra-low power solutions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_06543 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SparrowSNN: A Hardware/software Co-design for Energy Efficient ECG Classification Yan, Zhanglu Bai, Zhenyu Mitra, Tulika Wong, Weng-Fai Hardware Architecture Machine Learning Neural and Evolutionary Computing Signal Processing Deep learning has driven significant technological advancements, but its high energy consumption limits its use on battery-operated edge devices. Spiking Neural Networks (SNNs) offer promising reductions in inference-time energy consumption. However, existing neuromorphic architectures optimize scalable, many-core NoC execution, suited to large models but mismatched to edge devices, and their prevalent integrate-and-fire neurons re-read weights across \(T\) timesteps, inflating data-movement and dynamic-control energy. To address this challenge, we propose SparrowSNN, an optimized end-to-end design tailored for edge applications. SparrowSNN proposes: (1) a hardware-friendly spike activation function SSF (Sum-Spike-and-Fire); (2) a customizable $μ$W-level-power quantized hybrid ANN-SNN model that can be designed per application; (3) a compact and low-power reconfigurable ASIC architecture, supporting the aforementioned designs. Evaluated on biomedical MIT-BIH ECG and DEAP EEG datasets, SparrowSNN achieves state-of-the-art accuracy with $20\times$ to $100\times$ lower energy consumption, significantly outperforming existing ultra-low power solutions. |
| title | SparrowSNN: A Hardware/software Co-design for Energy Efficient ECG Classification |
| topic | Hardware Architecture Machine Learning Neural and Evolutionary Computing Signal Processing |
| url | https://arxiv.org/abs/2406.06543 |