ED-sKWS: Early-Decision Spiking Neural Networks for Rapid,and Energy-Efficient Keyword Spotting
<|reference_start|>ED-sKWS: Early-Decision Spiking Neural Networks for Rapid,and Energy-Efficient Keyword Spotting: Keyword Spotting (KWS) is essential in edge computing requiring rapid and energy-efficient responses. Spiking Neural Networks (SNNs) are well-suited for KWS for their efficiency and temporal capacity for speech. To further reduce the latency and energy consumption, this study introduces ED-sKWS, an SNN-based KWS model with an early-decision mechanism that can stop speech processing and output the result before the end of speech utterance. Furthermore, we introduce a Cumulative Temporal (CT) loss that can enhance prediction accuracy at both the intermediate and final timesteps. To evaluate early-decision performance, we present the SC-100 dataset including 100 speech commands with beginning and end timestamp annotation. Experiments on the Google Speech Commands v2 and our SC-100 datasets show that ED-sKWS maintains competitive accuracy with 61% timesteps and 52% energy consumption compared to SNN models without early-decision mechanism, ensuring rapid response and energy efficiency.<|reference_end|>
@article{song2024ed-skws:,
    title={ED-sKWS: Early-Decision Spiking Neural Networks for Rapid,and
  Energy-Efficient Keyword Spotting},
    author={Zeyang Song, Qianhui Liu, Qu Yang, Yizhou Peng, Haizhou Li},
    journal={arXiv preprint arXiv:2406.12726},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12726},
    primaryClass={cs.SD cs.AI eess.AS}
}