Severe constraints on memory and computation characterizing the Internet-of-Things (IoT) units may prevent the execution of Deep Learning (DL)-based solutions, which typically demand large memory and high processing load. In order to support a real-time execution of the considered DL model at the IoT unit level, DL solutions must be designed having in mind constraints on memory and processing capability exposed by the chosen IoT technology. In this paper, we introduce a design methodology aiming at allocating the execution of Convolutional Neural Networks (CNNs) on a distributed IoT application. Such a methodology is formalized as an optimization problem where the latency between the data-gathering phase and the subsequent decision-making o...
Deep Convolutional Neural Networks (DCNNs) achieve state of the art results compared to classic mach...
The promising results of deep learning (deep neural network) models in many applications such as spe...
Most of the research on deep neural networks so far has been focused on obtaining higher accuracy le...
Severe constraints on memory and computation characterizing the Internet-of-Things (IoT) units may p...
Deploying convolutional neural networks (CNNs) in embedded devices that operate at the edges of Inte...
Deep learning (DL) using large scale, high-quality IoT datasets can be computationally expensive. Ut...
The key impediments to deploying deep neural networks (DNN) in IoT edge environments lie in the gap ...
The Internet of Things (IoT) is utilizing Deep Learning (DL) for applications such as voice or image...
Internet of Things (IoT) infrastructures are more and more relying on multimedia sensors to provide ...
Motivated by the pervasiveness of artificial intelligence (AI) and the Internet of Things (IoT) in t...
Breakthroughs from the field of deep learning are radically changing how sensor data are interpreted...
Next-generation wireless networks have to be robust and self-sustained. Internet of things (IoT) is ...
Execution of deep learning solutions is mostly restricted to high performing computing platforms, e....
Existing deep learning systems in the Internet of Things (IoT) environments lack the ability of assi...
National audienceThe emergence of Machine Learning (ML) has increased exponentially in numerous appl...
Deep Convolutional Neural Networks (DCNNs) achieve state of the art results compared to classic mach...
The promising results of deep learning (deep neural network) models in many applications such as spe...
Most of the research on deep neural networks so far has been focused on obtaining higher accuracy le...
Severe constraints on memory and computation characterizing the Internet-of-Things (IoT) units may p...
Deploying convolutional neural networks (CNNs) in embedded devices that operate at the edges of Inte...
Deep learning (DL) using large scale, high-quality IoT datasets can be computationally expensive. Ut...
The key impediments to deploying deep neural networks (DNN) in IoT edge environments lie in the gap ...
The Internet of Things (IoT) is utilizing Deep Learning (DL) for applications such as voice or image...
Internet of Things (IoT) infrastructures are more and more relying on multimedia sensors to provide ...
Motivated by the pervasiveness of artificial intelligence (AI) and the Internet of Things (IoT) in t...
Breakthroughs from the field of deep learning are radically changing how sensor data are interpreted...
Next-generation wireless networks have to be robust and self-sustained. Internet of things (IoT) is ...
Execution of deep learning solutions is mostly restricted to high performing computing platforms, e....
Existing deep learning systems in the Internet of Things (IoT) environments lack the ability of assi...
National audienceThe emergence of Machine Learning (ML) has increased exponentially in numerous appl...
Deep Convolutional Neural Networks (DCNNs) achieve state of the art results compared to classic mach...
The promising results of deep learning (deep neural network) models in many applications such as spe...
Most of the research on deep neural networks so far has been focused on obtaining higher accuracy le...