Abstract Deep Neural Network (DNN) models for both resource-rich environments and
resource-constrained devices have become abundant in recent years. As of now, the …

In recent years, rapid advancements in both hardware and software technologies have
resulted in the ability to execute artificial intelligence (AI) algorithms on low-resource …

Driven by the recent growth in the fields of internet of things (IoT) and deep neural networks
(DNNs), DNN-powered IoT devices are expected to transform a variety of industrial …

This paper presents an in-depth analysis of the majority of the deep neural networks (DNNs)
proposed in the state of the art for image recognition. For each DNN, multiple performance …

Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI)
applications including computer vision, speech recognition, and robotics. While DNNs …

As real-time object detection systems, such as autonomous cars, need to process input
images acquired from multiple cameras, they face significant challenges in delivering …

In recent years, computational accessibility has enabled the use of Deep Neural Network
(DNN) for computer vision applications on devices with limited computational resources. We …

Over the past decade, a massive proliferation of machine learning algorithms has emerged,
from applications for surveillance to self-driving cars. The turning point occurred with the …

Nowadays, Deep Neural Networks are very popular for solving computer vision problems.
The motivation of this study is to explore the different deep learning-based architectures …

Deploying deep neural networks (DNNs) on edge devices provides efficient and effective
solutions for the real-world tasks. Edge devices have been used for collecting a large …