Quantized Guided Pruning For Efficient Hardware Implementations Of Convolutional Neural Networks

Hacene Ghouthi Boukli Elec, Gripon Vincent Elec, Arzel Matthieu Elec, Farrugia Nicolas Elec, Bengio Yoshua Diro. Arxiv 2018

[Paper]    
ARXIV CNN Quantisation Supervised

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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