Add to ORKGAccelerating and scaling the training of deep neural networks (DNNs) is critical to keep up with growing datasets, reduce training times, and enable training on memory-constrained problems where parallelism is necessary. In this thesis, I present a set of techniques that can leverage large high-performance computing systems for fast training of DNNs. I first introduce a suite of algorithms to exploit additional parallelism in convolutional layers when training, expanding beyond the standard sample-wise data-parallel approach to include spatial parallelism and channel and filter parallelism. Next, I present optimizations to communication frameworks to reduce communication overheads at large scales. Finally, I discuss communication quantizati...
As emerging deep neural network (DNN) models continue to grow in size, using large GPU clusters to t...
Deep neural networks (DNN) have recently achieved extraordinary results in domains like computer vis...
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...
Deep Neural Network (DNN) frameworks use distributed training to enable faster time to convergence a...
Deep neural network models are commonly used in various real-life applications due to their high pre...
Distributed training of Deep Neural Networks (DNN) is an important technique to reduce the training ...
Thesis (Master's)--University of Washington, 2018The recent success of Deep Neural Networks (DNNs) [...
Distributed training of Deep Neural Networks (DNN) is an important technique to reduce the training ...
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...
Deep Neural Network (DNN) frameworks use distributed training to enable faster time to convergence a...
Deep Neural Networks (DNNs) enable computers to excel across many different applications such as ima...
Deep neural networks are trained by solving huge optimization problems with large datasets and milli...
Memory usage is becoming an increasingly pressing bottleneck in the training process of Deep Neural ...
As emerging deep neural network (DNN) models continue to grow in size, using large GPU clusters to t...
Deep neural networks (DNN) have recently achieved extraordinary results in domains like computer vis...
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...
Deep Neural Network (DNN) frameworks use distributed training to enable faster time to convergence a...
Deep neural network models are commonly used in various real-life applications due to their high pre...
Distributed training of Deep Neural Networks (DNN) is an important technique to reduce the training ...
Thesis (Master's)--University of Washington, 2018The recent success of Deep Neural Networks (DNNs) [...
Distributed training of Deep Neural Networks (DNN) is an important technique to reduce the training ...
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...
Deep Neural Network (DNN) frameworks use distributed training to enable faster time to convergence a...
Deep Neural Networks (DNNs) enable computers to excel across many different applications such as ima...
Deep neural networks are trained by solving huge optimization problems with large datasets and milli...
Memory usage is becoming an increasingly pressing bottleneck in the training process of Deep Neural ...
As emerging deep neural network (DNN) models continue to grow in size, using large GPU clusters to t...
Deep neural networks (DNN) have recently achieved extraordinary results in domains like computer vis...
Deep neural networks (DNN) have recently achieved extraordinary results in domains like computer vis...