  Nowadays, as edge devices such as smartphones become prevalent, there are
increasing demands for personalized services. However, traditional
personalization methods are not suitable for edge devices because retraining or
finetuning is needed with limited personal data. Also, a full model might be
too heavy for edge devices with limited resources. Unfortunately, model
compression methods which can handle the model complexity issue also require
the retraining phase. These multiple training phases generally need huge
computational cost during on-device learning which can be a burden to edge
devices. In this work, we propose a dynamic personalization method called
prototype-based personalized pruning (PPP). PPP considers both ends of
personalization and model efficiency. After training a network, PPP can easily
prune the network with a prototype representing the characteristics of personal
data and it performs well without retraining or finetuning. We verify the
usefulness of PPP on a couple of tasks in computer vision and Keyword spotting.

Machine Learning