Add to ORKGDistributed training of Deep Neural Networks (DNN) is an important technique to reduce the training time of large DNNs for a wide range of applications. In existing distributed training approaches, however, the communication time to periodically exchange parameters (i.e., weights) and gradients among computer nodes over the network constitutes a large fraction of the total training time. To reduce the communication time, we propose an algorithm/hardware co-design, INCEPTIONN. More specifically, observing that gradients are much more tolerant to precision loss than parameters, we first propose a gradient-centric distributed training algorithm. As designed to exchange only gradients among nodes in a distributed manner, it can transfer less ...
With increasing data and model complexities, the time required to train neural networks has become p...
Compressed communication, in the form of sparsification or quantization of stochastic gradients, is ...
The distributed training of deep learning models faces two issues: efficiency and privacy. First of ...
Distributed training of Deep Neural Networks (DNN) is an important technique to reduce the training ...
Training a deep neural network (DNN) with a single machine consumes much time. To accelerate the tra...
Accelerating and scaling the training of deep neural networks (DNNs) is critical to keep up with gro...
Training a deep neural network (DNN) with a single machine consumes much time. To accelerate the tra...
Highly distributed training of Deep Neural Networks (DNNs) on future compute platforms (offering 100...
In federated learning (FL), a global model is trained at a Parameter Server (PS) by aggregating mode...
Although the distributed machine learning methods can speed up the training of large deep neural net...
With increasing data and model complexities, the time required to train neural networks has become p...
To support large-scale machine learning, distributed training is a promising approach as large-scale...
Data parallel training is commonly used for scaling distributed Deep Neural Network ( DNN ) training...
Data parallel training is commonly used for scaling distributed Deep Neural Network ( DNN ) training...
Deep Neural Networks (DNNs) enable computers to excel across many different applications such as ima...
With increasing data and model complexities, the time required to train neural networks has become p...
Compressed communication, in the form of sparsification or quantization of stochastic gradients, is ...
The distributed training of deep learning models faces two issues: efficiency and privacy. First of ...
Distributed training of Deep Neural Networks (DNN) is an important technique to reduce the training ...
Training a deep neural network (DNN) with a single machine consumes much time. To accelerate the tra...
Accelerating and scaling the training of deep neural networks (DNNs) is critical to keep up with gro...
Training a deep neural network (DNN) with a single machine consumes much time. To accelerate the tra...
Highly distributed training of Deep Neural Networks (DNNs) on future compute platforms (offering 100...
In federated learning (FL), a global model is trained at a Parameter Server (PS) by aggregating mode...
Although the distributed machine learning methods can speed up the training of large deep neural net...
With increasing data and model complexities, the time required to train neural networks has become p...
To support large-scale machine learning, distributed training is a promising approach as large-scale...
Data parallel training is commonly used for scaling distributed Deep Neural Network ( DNN ) training...
Data parallel training is commonly used for scaling distributed Deep Neural Network ( DNN ) training...
Deep Neural Networks (DNNs) enable computers to excel across many different applications such as ima...
With increasing data and model complexities, the time required to train neural networks has become p...
Compressed communication, in the form of sparsification or quantization of stochastic gradients, is ...
The distributed training of deep learning models faces two issues: efficiency and privacy. First of ...