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GPU Domain Specialization via Composable On-Package Architecture
As GPUs scale their low precision matrix math throughput to boost deep learning (DL) performance, they upset the balance between math throughput and memory system capabilities.
DeLTA: GPU Performance Model for Deep Learning Applications with In-depth Memory System Traffic Analysis
Training convolutional neural networks (CNNs) requires intense compute throughput and high memory bandwidth.
Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks
Popular deep learning frameworks require users to fine-tune their memory usage so that the training data of a deep neural network (DNN) fits within the GPU physical memory.
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