On the Reduction of Computational Complexity of Deep Convolutional Neural Networks.
Entropy (Basel, Switzerland)
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Maji, P., & Mullins, R. (2018). On the Reduction of Computational Complexity of Deep Convolutional Neural Networks.. Entropy (Basel, Switzerland), 20 (4)https://doi.org/10.3390/e20040305
Deep convolutional neural networks (ConvNets), which are at the heart of many new emerging applications, achieve remarkable performance in audio and visual recognition tasks. Unfortunately, achieving accuracy often requires very significant computation, limiting deployability. In modern ConvNets it is typical for the convolution layers to consume the vast majority of the compute resources during inference. This has made the acceleration of these layers an important research and industrial goal. In this paper, we examine the effects of co-optimizing the internal structures of the convolutional layers and underlying implementation of fundamental convolution operation. We demonstrate that a combination of these methods can have a big impact on the overall speed-up of a ConvNet, achieving a tenfold increase over baseline. We also introduce a new class of fast 1D (one dimensional) convolutions for ConvNets using the Toom-Cook algorithm. We show that our proposed scheme is mathematically well grounded, robust, does not require any time-consuming retraining, and still achieves speed-ups solely from convolutional layers with no loss in baseline accuracy.
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External DOI: https://doi.org/10.3390/e20040305
This record's URL: https://www.repository.cam.ac.uk/handle/1810/278187
Attribution 4.0 International
Licence URL: http://creativecommons.org/licenses/by/4.0/
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