Recently, a number of classification techniques have been introduced. However, processing
large dataset in a reasonable time has become a major challenge. This made classification …

The growing prevalence and computational demands of Artificial Intelligence (AI) workloads
has led to widespread use of hardware accelerators in their execution. Scaling the …

Abstract Recently, Deep Learning (DL) applications are getting more and more involved in
different fields. Deploying such Deep Neural Networks (DNN) on embedded devices is still a …

Global channel pruning (GCP) aims to remove a subset of channels (filters) across different
layers from a deep model without hurting the performance. Previous works focus on either …

We present a novel deep learning architecture in which the convolution operation leverages
heterogeneous kernels. The proposed HetConv (Heterogeneous Kernel-Based …

We present a filter correlation based model compression approach for deep convolutional
neural networks. Our approach iteratively identifies pairs of filters with the largest pairwise …

Object detection is one of the main tasks of computer vision. Object detection algorithms
usually rely on deep convolutional neural networks, which require the host device to have …

Indoor object detection and recognition present one of the most crucial tasks for computer
vision and robotic systems. Develo** new intelligent autonomous robots is required in …

The past few years have witnessed the dramatic increase in layers of convolutional neural
networks (CNN). Most studies focused on the CNN's vertical structure design (eg residual …

Abstract Convolution Neural Networks (CNN) have been extremely successful in solving
intensive computer vision tasks. The convolutional filters used in CNNs have played a major …