PhD Forum Abstract: Understanding Deep Model Compression for IoT Devices

Today’s deep neural networks require substantial computation resources for their training, storage, and inference, which limits their effective use on resource-constrained devices. Many recent research activities explore different options for compressing and optimizing deep models for the Internet of Things (IoT). Our work so far has focused on two important aspects of deep neural network compression: class-dependent model compression and explainable compression. We shortly summarize our contributions and conclude with an outline of our future research directions.