AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Temporal graph neural networks (TGNNs) achieve superior performance in many dynamic graph learning tasks. These models use node state vectors (i.e., memories) to capture and record graph changes (i.e., events), and then exploit these memories to assist prediction. However, this memory-centric design makes multi-GPU tra...
Training high-resolution AI-based Earth forecasting models is memory-intensive. Window-based Swin Transformers reduce the quadratic cost of global attention, but existing distributed systems such as AERIS primarily target pixel-level models and do not jointly support convolutional sampling modules and shifted-window ex...
By making DWC compute-efficient, Flash-DWC promotes the use of larger and more expressive filters and extends forward and backward propagation to both forward and backward propagation for efficient end-to-end training.
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