Runtime Elastic Tensor Selection

Runtime elastic tensor selection focuses on optimizing the efficiency of tensor computations, particularly within machine learning models, by dynamically choosing which parts of a model are actively used during training or inference. Current research explores techniques like reinforcement learning-based auto-schedulers and asynchronous multi-model approaches to achieve this dynamic selection, aiming to improve training speed, reduce energy consumption, and enhance inference performance. This research is significant because it addresses the computational bottlenecks inherent in large-scale machine learning, impacting both the development of more efficient algorithms and the deployment of AI applications on resource-constrained devices.