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arXiv · 1812.11337

Quantized Guided Pruning for Efficient Hardware Implementations of Convolutional Neural Networks

Ghouthi Boukli Hacene·Vincent Gripon·Matthieu Arzel·Nicolas Farrugia·Yoshua Bengio
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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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Ghouthi Boukli Hacene, Vincent Gripon, Matthieu Arzel, Nicolas Farrugia, Yoshua Bengio. 2018-12-29. Quantized Guided Pruning for Efficient Hardware Implementations of Convolutional Neural Networks. https://arxiv.org/abs/1812.11337

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