OnDA: On-device Channel Pruning for Efficient Personalized Keyword Spotting

Always-on keyword spotting (KWS) demands on-device adaptation to cope with user-and environment-specific distribution shifts under tight latency and energy budgets.This paper proposes, for the first time, coupling weight adaptation (i.e., on-device training) with architectural adaptation, in the form of online structured channel pruning, for personalized on-device KWS.Starting from a state-of-the-art self-learning personalized KWS pipeline, we compare data-agnostic and data-aware pruning criteria applied on in-field pseudo-labelled user data.On the HeySnips and HeySnapdragon datasets, we achieve up to 9.63× model-size compression with respect to unpruned baselines at iso-task performance, measured as the accuracy at 0.5 false alarms per hour.When deploying our adaptation pipeline on a Jetson Orin Nano embedded GPU, we achieve up to 1.52×/1.57×and 1.64×/1.77×latency and energy-consumption improvements during online training/inference compared to weights-only adaptation.