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In today's world, a vast amount of data is being generated by edge devicesthat can be used as valuable training data to improve the performance ofmachine learning algorithms in terms of the achieved accuracy or to reduce thecompute requirements of the model. However, due to user data privacy concernsas well as storage and communication bandwidth limitations, this data cannot bemoved from the device to the data centre for further improvement of the modeland subsequent deployment. As such there is a need for increased edgeintelligence, where the deployed models can be fine-tuned on the edge, leadingto improved accuracy and/or reducing the model's workload as well as its memoryand power footprint. In the case of Convolutional Neural Networks (CNNs), boththe weights of the network as well as its topology can be tuned to adapt to thedata that it processes. This paper provides a first step towards enabling CNNfinetuning on an edge device based on structured pruning. It explores theperformance gains and costs of doing so and presents an extensible open-sourceframework that allows the deployment of such approaches on a wide range ofnetwork architectures and devices. The results show that on average, data-awarepruning with retraining can provide 10.2pp increased accuracy over a wide rangeof subsets, networks and pruning levels with a maximum improvement of 42.0ppover pruning and retraining in a manner agnostic to the data being processed bythe network.

Now that I can see, I can improve: Enabling data-driven finetuning of CNNs on the edge