Add to ORKGWe propose a method to parallelize the training of a convolutional neural network by using a CUDA-based cluster. We attain a sub-stantial increase in the performance of the algorithm itself. We re-search the feasibility of using batch versus online mode training and provide a performance comparison between them. Furthermore, we propose an implementation of an alternative algorithm to compute local gradients which increases the level of parallelism. To con-clude, we give a set of best practices for implementing Convolu-tional Neural Networks on the cluster
Long training times and non-ideal performance have been a big impediment in further continuing the u...
We propose a new integrated method of exploiting model, batch and domain parallelism for the trainin...
Deep neural network models can achieve greater performance in numerous machine learning tasks by rai...
The convolutional neural networks (CNNs) have proven to be powerful classification tools in tasks th...
I present a new way to parallelize the training of convolutional neural networks across multiple GPU...
Deep neural networks have gained popularity in recent years, obtaining outstanding results in a wide...
Convolutional neural networks [3] have proven useful in many domains, including computer vi-sion [1,...
Convolutional neural networks [3] have proven useful in many domains, including computer vi-sion [1,...
We present a technique for parallelizing the training of neural networks. Our technique is designed ...
Deep learning algorithms base their success on building high learning capacity models with millions ...
Deep Neural Network (DNN) frameworks use distributed training to enable faster time to convergence a...
The purpose of this thesis is to determine the performance of convolutional neural networks in class...
The Graphics Processing Units (GPUs) have been used for accelerating graphic calculations as well as...
A parallel Back-Propagation(BP) neural network training technique using Compute Unified Device Archi...
Long training times and non-ideal performance have been a big impediment in further continuing the u...
Long training times and non-ideal performance have been a big impediment in further continuing the u...
We propose a new integrated method of exploiting model, batch and domain parallelism for the trainin...
Deep neural network models can achieve greater performance in numerous machine learning tasks by rai...
The convolutional neural networks (CNNs) have proven to be powerful classification tools in tasks th...
I present a new way to parallelize the training of convolutional neural networks across multiple GPU...
Deep neural networks have gained popularity in recent years, obtaining outstanding results in a wide...
Convolutional neural networks [3] have proven useful in many domains, including computer vi-sion [1,...
Convolutional neural networks [3] have proven useful in many domains, including computer vi-sion [1,...
We present a technique for parallelizing the training of neural networks. Our technique is designed ...
Deep learning algorithms base their success on building high learning capacity models with millions ...
Deep Neural Network (DNN) frameworks use distributed training to enable faster time to convergence a...
The purpose of this thesis is to determine the performance of convolutional neural networks in class...
The Graphics Processing Units (GPUs) have been used for accelerating graphic calculations as well as...
A parallel Back-Propagation(BP) neural network training technique using Compute Unified Device Archi...
Long training times and non-ideal performance have been a big impediment in further continuing the u...
Long training times and non-ideal performance have been a big impediment in further continuing the u...
We propose a new integrated method of exploiting model, batch and domain parallelism for the trainin...
Deep neural network models can achieve greater performance in numerous machine learning tasks by rai...