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Found 5 papers, 3 papers with code
12 mJ per Class On-Device Online Few-Shot Class-Incremental Learning
In this work, we introduce Online Few-Shot Class-Incremental Learning (O-FSCIL), based on a lightweight model consisting of a pretrained and metalearned feature extractor and an expandable explicit memory storing the class prototypes.
On-Device Domain Learning for Keyword Spotting on Low-Power Extreme Edge Embedded Systems
Keyword spotting accuracy degrades when neural networks are exposed to noisy environments.
Boosting keyword spotting through on-device learnable user speech characteristics
Keyword spotting systems for always-on TinyML-constrained applications require on-site tuning to boost the accuracy of offline trained classifiers when deployed in unseen inference conditions.
Towards On-device Domain Adaptation for Noise-Robust Keyword Spotting
The accuracy of a keyword spotting model deployed on embedded devices often degrades when the system is exposed to real environments with significant noise.
MS-RANAS: Multi-Scale Resource-Aware Neural Architecture Search
Neural Architecture Search (NAS) has proved effective in offering outperforming alternatives to handcrafted neural networks.
