Deep learning has been widely adopted for different applications of artificial intelligence-speech recognition, natural language processing, computer vision etc. The growing size of Deep Neural Networks (DNNs) has compelled the researchers to design memory efficient and performance optimal algorithms. Apart from algorithmic improvements, specialized hardware like Graphics Processing Units (GPUs) are being widely employed to accelerate the training and inference phases of deep networks. However, the limited GPU memory capacity limits the upper bound on the size of networks that can be offloaded to and trained using GPUs. vDNN addresses the GPU memory bottleneck issue and provides a solution which enables training of deep networks that are la...
Deep neural network models are commonly used in various real-life applications due to their high pre...
International audienceTraining Deep Neural Networks is known to be an expensive operation, both in t...
Deep learning is an emerging workload in the field of HPC. This powerful method of resolution is abl...
The most widely used machine learning frameworks require users to carefully tune their memory usage ...
© 2018 ACM. Going deeper and wider in neural architectures improves their accuracy, while the limite...
Recently, machine learning, especially deep learning, has been a core algorithm to be widely used in...
Deep Learning, specifically Deep Neural Networks (DNNs), is stressing storage systems in new...
Popular deep learning frameworks require users to fine-tune their memory usage so that the training ...
Deep neural networks have been continuously evolving towards larger and more complex models to solve...
The size of neural networks a GPU can train is limited by the GPU’s memory capacity. Although GPU vi...
Memory usage is becoming an increasingly pressing bottleneck in the training process of Deep Neural ...
International audienceThe resource-hungry and time-consuming process of training Deep Neural Network...
Accelerating and scaling the training of deep neural networks (DNNs) is critical to keep up with gro...
GPUs are the workhorse in modern server infrastructure fueling advances in a number of compute-inten...
Neural networks get more difficult and longer time to train if the depth become deeper. As deep neur...
Deep neural network models are commonly used in various real-life applications due to their high pre...
International audienceTraining Deep Neural Networks is known to be an expensive operation, both in t...
Deep learning is an emerging workload in the field of HPC. This powerful method of resolution is abl...
The most widely used machine learning frameworks require users to carefully tune their memory usage ...
© 2018 ACM. Going deeper and wider in neural architectures improves their accuracy, while the limite...
Recently, machine learning, especially deep learning, has been a core algorithm to be widely used in...
Deep Learning, specifically Deep Neural Networks (DNNs), is stressing storage systems in new...
Popular deep learning frameworks require users to fine-tune their memory usage so that the training ...
Deep neural networks have been continuously evolving towards larger and more complex models to solve...
The size of neural networks a GPU can train is limited by the GPU’s memory capacity. Although GPU vi...
Memory usage is becoming an increasingly pressing bottleneck in the training process of Deep Neural ...
International audienceThe resource-hungry and time-consuming process of training Deep Neural Network...
Accelerating and scaling the training of deep neural networks (DNNs) is critical to keep up with gro...
GPUs are the workhorse in modern server infrastructure fueling advances in a number of compute-inten...
Neural networks get more difficult and longer time to train if the depth become deeper. As deep neur...
Deep neural network models are commonly used in various real-life applications due to their high pre...
International audienceTraining Deep Neural Networks is known to be an expensive operation, both in t...
Deep learning is an emerging workload in the field of HPC. This powerful method of resolution is abl...