Distributed deep learning has emerged as an essential approach for training large-scale deep neural networks by utilising multiple computational nodes. This methodology partitions the workload either ...

AI models are rapidly increasing in complexity, demanding more powerful computing resources for effective training and inference. This trend has sparked significant interest in scaling computational ...