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.
@article{arxiv.1812.11337,
title = {Quantized Guided Pruning for Efficient Hardware Implementations of Convolutional Neural Networks},
author = {Ghouthi Boukli Hacene and Vincent Gripon and Matthieu Arzel and Nicolas Farrugia and Yoshua Bengio},
journal= {arXiv preprint arXiv:1812.11337},
year = {2019}
}