Published March 16, 2022 | Version v1
Conference paper Open

A data-aware dictionary-learning based technique for the acceleration of deep convolutional networks

  • 1. University of Patras, Athena Research Center, Greece
  • 2. Athena Research Center, Greece

Description

The deployment of high performing deep learning models on platforms of limited resources is currently an active area of research. Among the main directions followed so far, pre-trained neural networks are accelerated and compressed by appropriately modifying their structure and / or parameters. Capitalizing on a recently proposed codebook of a special structure that can be utilized in the frame of the so-called weight sharing methods, this paper describes a “data-driven” technique for designing such a codebook. The performance of the technique, in terms of the observed representation error and classification accuracy versus the achieved acceleration ratio, is demonstrated by considering the VGG16 and the ResNet18 models, pre-trained on the ILSVRC2012 dataset.

Files

A_data-aware_dictionary-learning_based_technique_for_the_acceleration_of_deep_convolutional_networks.pdf

Additional details

Funding

CPSoSaware – Cross-layer cognitive optimization tools & methods for the lifecycle support of dependable CPSoS 871738
European Commission