no code implementations • 12 Mar 2024 • Cristian Cioflan, Lukas Cavigelli, Luca Benini
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.
1 code implementation • 12 Mar 2024 • Yoga Esa Wibowo, Cristian Cioflan, Thorir Mar Ingolfsson, Michael Hersche, Leo Zhao, Abbas Rahimi, Luca Benini
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.
no code implementations • 12 Mar 2024 • Cristian Cioflan, Lukas Cavigelli, Manuele Rusci, Miguel de Prado, Luca Benini
Keyword spotting accuracy degrades when neural networks are exposed to noisy environments.
1 code implementation • IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) 2022 • Cristian Cioflan, Lukas Cavigelli, Manuele Rusci, Miguel de Prado, Luca Benini
The accuracy of a keyword spotting model deployed on embedded devices often degrades when the system is exposed to real environments with significant noise.
1 code implementation • 29 Sep 2020 • Cristian Cioflan, Radu Timofte
Neural Architecture Search (NAS) has proved effective in offering outperforming alternatives to handcrafted neural networks.