© 2018 ACM. Going deeper and wider in neural architectures improves their accuracy, while the limited GPU DRAM places an undesired restriction on the network design domain. Deep Learning (DL) practitioners either need to change to less desired network architectures, or nontrivially dissect a network across multiGPUs. These distract DL practitioners from concentrating on their original machine learning tasks. We present SuperNeurons: a dynamic GPU memory scheduling runtime to enable the network training far beyond the GPU DRAM capacity. SuperNeurons features 3 memory optimizations, Liveness Analysis, Unified Tensor Pool, and Cost-Aware Recomputation; together they effectively reduce the network-wide peak memory usage down to the maximal memo...
Neural networks get more difficult and longer time to train if the depth become deeper. As deep neur...
Accelerating and scaling the training of deep neural networks (DNNs) is critical to keep up with gro...
As emerging deep neural network (DNN) models continue to grow in size, using large GPU clusters to t...
© 2018 ACM. Going deeper and wider in neural architectures improves their accuracy, while the limite...
Deep learning has been widely adopted for different applications of artificial intelligence-speech r...
The most widely used machine learning frameworks require users to carefully tune their memory usage ...
© 2021 by The USENIX Association.Deep neural networks (DNNs) are widely used in various AI applicati...
Deep neural network models are commonly used in various real-life applications due to their high pre...
Memory usage is becoming an increasingly pressing bottleneck in the training process of Deep Neural ...
The size of neural networks a GPU can train is limited by the GPU’s memory capacity. Although GPU vi...
GPUs are the workhorse in modern server infrastructure fueling advances in a number of compute-inten...
Deep neural networks have been continuously evolving towards larger and more complex models to solve...
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...
Deep neural networks have gained popularity in recent years, obtaining outstanding results in a wide...
Neural networks get more difficult and longer time to train if the depth become deeper. As deep neur...
Accelerating and scaling the training of deep neural networks (DNNs) is critical to keep up with gro...
As emerging deep neural network (DNN) models continue to grow in size, using large GPU clusters to t...
© 2018 ACM. Going deeper and wider in neural architectures improves their accuracy, while the limite...
Deep learning has been widely adopted for different applications of artificial intelligence-speech r...
The most widely used machine learning frameworks require users to carefully tune their memory usage ...
© 2021 by The USENIX Association.Deep neural networks (DNNs) are widely used in various AI applicati...
Deep neural network models are commonly used in various real-life applications due to their high pre...
Memory usage is becoming an increasingly pressing bottleneck in the training process of Deep Neural ...
The size of neural networks a GPU can train is limited by the GPU’s memory capacity. Although GPU vi...
GPUs are the workhorse in modern server infrastructure fueling advances in a number of compute-inten...
Deep neural networks have been continuously evolving towards larger and more complex models to solve...
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...
Deep neural networks have gained popularity in recent years, obtaining outstanding results in a wide...
Neural networks get more difficult and longer time to train if the depth become deeper. As deep neur...
Accelerating and scaling the training of deep neural networks (DNNs) is critical to keep up with gro...
As emerging deep neural network (DNN) models continue to grow in size, using large GPU clusters to t...