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Quantized Guided Pruning for Efficient Hardware Implementations of Convolutional Neural Networks

Abstract : Convolutional Neural Networks (CNNs) are state-of-the-art in numerous computer vision tasks such as object classification and detection. However, the large amount of parameters they contain leads to a high computational complexity and strongly limits their usability in budget-constrained devices such as embedded devices. In this paper, we propose a combination of a new pruning technique and a quantization scheme that effectively reduce the complexity and memory usage of convolutional layers of CNNs, and replace the complex convolutional operation by a low-cost multiplexer. We perform experiments on the CIFAR10, CIFAR100 and SVHN and show that the proposed method achieves almost state-of-the-art accuracy, while drastically reducing the computational and memory footprints. We also propose an efficient hardware architecture to accelerate CNN operations. The proposed hardware architecture is a pipeline and accommodates multiple layers working at the same time to speed up the inference process.
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Contributor : Ghouthi BOUKLI HACENE Connect in order to contact the contributor
Submitted on : Tuesday, December 25, 2018 - 10:58:11 PM
Last modification on : Friday, August 5, 2022 - 2:54:52 PM
Long-term archiving on: : Tuesday, March 26, 2019 - 2:45:09 PM


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  • HAL Id : hal-01965304, version 1
  • ARXIV : 1812.11337


Ghouthi Boukli Hacene, Vincent Gripon, Matthieu Arzel, Nicolas Farrugia, Yoshua Bengio. Quantized Guided Pruning for Efficient Hardware Implementations of Convolutional Neural Networks. 2018. ⟨hal-01965304⟩



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