A New Clustering-Based Technique for the Acceleration of Deep Convolutional Networks

• URL: http://arxiv.org/abs/2107.09095v1
• Date: Mon, 19 Jul 2021 18:22:07 GMT
• Title: A New Clustering-Based Technique for the Acceleration of Deep Convolutional Networks
• Authors: Erion-Vasilis Pikoulis, Christos Mavrokefalidis, Aris S. Lalos
• Abstract summary: Model Compression and Acceleration (MCA) techniques are used to transform large pre-trained networks into smaller models. We propose a clustering-based approach that is able to increase the number of employed centroids/representatives. This is achieved by imposing a special structure to the employed representatives, which is enabled by the particularities of the problem at hand.
• Score: 2.7393821783237184
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning and especially the use of Deep Neural Networks (DNNs) provides impressive results in various regression and classification tasks. However, to achieve these results, there is a high demand for computing and storing resources. This becomes problematic when, for instance, real-time, mobile applications are considered, in which the involved (embedded) devices have limited resources. A common way of addressing this problem is to transform the original large pre-trained networks into new smaller models, by utilizing Model Compression and Acceleration (MCA) techniques. Within the MCA framework, we propose a clustering-based approach that is able to increase the number of employed centroids/representatives, while at the same time, have an acceleration gain compared to conventional, $k$-means based approaches. This is achieved by imposing a special structure to the employed representatives, which is enabled by the particularities of the problem at hand. Moreover, the theoretical acceleration gains are presented and the key system hyper-parameters that affect that gain, are identified. Extensive evaluation studies carried out using various state-of-the-art DNN models trained in image classification, validate the superiority of the proposed method as compared for its use in MCA tasks.

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