公开(公告)号：US20230111375A1

公开(公告)日：2023-04-13

申请号：US17724819

申请日：2022-04-20

Applicant: NVIDIA Corporation

Inventor： Jason Lavar Clemons ,  Kavya Sreedhar ,  Stephen W. Keckler

IPC: G06N3/08 ,  G06N3/04 ,  G06F11/34

Abstract: A neural network model is augmented for dynamic configuration and execution in real-time according to performance constraints. In an embodiment, the neural network model is a transformer neural network model. The performance constraints may include a metric, such as inferencing execution time or energy consumption and a target value for the metric. The augmented neural network model is characterized for various configurations and settings are determined corresponding to a variety of the performance constraints. One or more performance constraints may be provided as an input to dynamically select a configuration of the augmented neural network model. Through dynamic configuration, the augmented neural network model may adapt to real-time changes in the performance constraints. However, the trained weights for an original (before augmentation) neural network model may be used by the augmented neural network model without modification.