GPU Architecture

GPU architecture research centers on optimizing hardware and software to efficiently handle the massive computational demands of modern machine learning models, particularly deep neural networks and graph convolutional networks. Current efforts focus on improving memory efficiency through techniques like activation compression and data reuse optimization, as well as maximizing computational throughput via kernel fusion and specialized instructions like those found in the Hopper architecture. These advancements are crucial for enabling the training and deployment of increasingly complex models in various fields, ranging from natural language processing to computer vision, by reducing training times and improving performance on resource-constrained systems.