Quantized Guided Pruning for Efficient Hardware Implementations of Convolutional Neural Networks
This addresses the problem of efficient CNN deployment for embedded devices, representing an incremental improvement through a hybrid method.
The paper tackles the high computational complexity and memory usage of CNNs for deployment on budget-constrained devices by proposing a combination of pruning and quantization, achieving near state-of-the-art accuracy on CIFAR10, CIFAR100, and SVHN while drastically reducing computational and memory footprints.
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.