Add to ORKGDeep neural networks (DNN) have recently achieved extraordinary results in domains like computer vision and speech recognition. An essential element for this success has been the introduction of high performance computing (HPC) techniques in the critical step of training the neural network. This paper describes the implementation and analysis of a network-agnostic and convergence-invariant coarse-grain parallelization of the DNN training algorithm. The coarse-grain parallelization is achieved through the exploitation of the batch-level parallelism. This strategy is independent from the support of specialized and optimized libraries. Therefore, the optimization is immediately available for accelerating the DNN training. The proposal is compa...
In recent years, machine learning (ML) and, more noticeably, deep learning (DL), have be- come incre...
We propose a new integrated method of exploiting model, batch and domain parallelism for the trainin...
We propose a new integrated method of exploiting model, batch and domain parallelism for the trainin...
Deep neural networks (DNN) have recently achieved extraordinary results in domains like computer vis...
Accelerating and scaling the training of deep neural networks (DNNs) is critical to keep up with gro...
Neural networks get more difficult and longer time to train if the depth become deeper. As deep neur...
Deep learning, a sub-topic of machine learning inspired by biology, have achieved wide attention in ...
Deep learning, a sub-topic of machine learning inspired by biology, have achieved wide attention in ...
Deep learning, a sub-topic of machine learning inspired by biology, have achieved wide attention in ...
Deep Neural Network (DNN) frameworks use distributed training to enable faster time to convergence a...
Thesis (Master's)--University of Washington, 2018The recent success of Deep Neural Networks (DNNs) [...
Deep neural networks (DNNs) have emerged as successful solutions for variety of artificial intellige...
Convolutional deep neural networks (CNNs) has been shown to perform well in difficult learning tasks...
Convolutional deep neural networks (CNNs) has been shown to perform well in difficult learning tasks...
In recent years, machine learning (ML) and, more noticeably, deep learning (DL), have be- come incre...
In recent years, machine learning (ML) and, more noticeably, deep learning (DL), have be- come incre...
We propose a new integrated method of exploiting model, batch and domain parallelism for the trainin...
We propose a new integrated method of exploiting model, batch and domain parallelism for the trainin...
Deep neural networks (DNN) have recently achieved extraordinary results in domains like computer vis...
Accelerating and scaling the training of deep neural networks (DNNs) is critical to keep up with gro...
Neural networks get more difficult and longer time to train if the depth become deeper. As deep neur...
Deep learning, a sub-topic of machine learning inspired by biology, have achieved wide attention in ...
Deep learning, a sub-topic of machine learning inspired by biology, have achieved wide attention in ...
Deep learning, a sub-topic of machine learning inspired by biology, have achieved wide attention in ...
Deep Neural Network (DNN) frameworks use distributed training to enable faster time to convergence a...
Thesis (Master's)--University of Washington, 2018The recent success of Deep Neural Networks (DNNs) [...
Deep neural networks (DNNs) have emerged as successful solutions for variety of artificial intellige...
Convolutional deep neural networks (CNNs) has been shown to perform well in difficult learning tasks...
Convolutional deep neural networks (CNNs) has been shown to perform well in difficult learning tasks...
In recent years, machine learning (ML) and, more noticeably, deep learning (DL), have be- come incre...
In recent years, machine learning (ML) and, more noticeably, deep learning (DL), have be- come incre...
We propose a new integrated method of exploiting model, batch and domain parallelism for the trainin...
We propose a new integrated method of exploiting model, batch and domain parallelism for the trainin...