Add to ORKGConvolutional neural networks (CNNs) have recently attracted considerable attention due to their outstanding accuracy in applications, such as image recognition and natural language processing. While one advantage of the CNNs over other types of neural networks is their reduced computational cost, faster execution is still desired for both training and inference. Since convolution operations pose most of the execution time, multiple algorithms were and are being developed with the aim of accelerating this type of operations. However, due to the wide range of convolution parameter configurations used in the CNNs and the possible data type representations, it is not straightforward to assess in advance which of the available algorithms will b...
Convolution computation is a common operation in deep neural networks (DNNs) and is often responsibl...
This work is focused on the pruning of some convolutional neural networks (CNNs) and improving their...
The convolutional neural networks (CNNs) have proven to be powerful classification tools in tasks th...
Convolutional neural networks (CNNs) have recently attracted considerable attention due to their out...
Convolutional neural network (CNN) is an important deep learning method. The convolution operation t...
Convolution is the most computationally intensive task of the Convolutional Neural Network (CNN). It...
Open-source deep learning tools has been distributed numerously and has gain popularity in the past ...
Deep learning is a branch of machine learning that aims to extract multiple simple features from da...
The main contribution of this paper is to show efficient implementations of the convolution-pooling ...
When asked to implement a neural network application, the decision concerning what hardware platform...
Recent years saw an increasing success in the application of deep learning methods across various do...
[Abstract] Improving the performance of the convolution operation has become a key target for High P...
Convolutional deep neural networks (CNNs) has been shown to perform well in difficult learning tasks...
The purpose of this thesis is to determine the performance of convolutional neural networks in class...
Image processing-based artificial intelligence algorithm is a critical task, and the implementation ...
Convolution computation is a common operation in deep neural networks (DNNs) and is often responsibl...
This work is focused on the pruning of some convolutional neural networks (CNNs) and improving their...
The convolutional neural networks (CNNs) have proven to be powerful classification tools in tasks th...
Convolutional neural networks (CNNs) have recently attracted considerable attention due to their out...
Convolutional neural network (CNN) is an important deep learning method. The convolution operation t...
Convolution is the most computationally intensive task of the Convolutional Neural Network (CNN). It...
Open-source deep learning tools has been distributed numerously and has gain popularity in the past ...
Deep learning is a branch of machine learning that aims to extract multiple simple features from da...
The main contribution of this paper is to show efficient implementations of the convolution-pooling ...
When asked to implement a neural network application, the decision concerning what hardware platform...
Recent years saw an increasing success in the application of deep learning methods across various do...
[Abstract] Improving the performance of the convolution operation has become a key target for High P...
Convolutional deep neural networks (CNNs) has been shown to perform well in difficult learning tasks...
The purpose of this thesis is to determine the performance of convolutional neural networks in class...
Image processing-based artificial intelligence algorithm is a critical task, and the implementation ...
Convolution computation is a common operation in deep neural networks (DNNs) and is often responsibl...
This work is focused on the pruning of some convolutional neural networks (CNNs) and improving their...
The convolutional neural networks (CNNs) have proven to be powerful classification tools in tasks th...