Speaker
Sitian Qian
(Peking University (CN))
Description
Recent years have witnessed the enormous success of the transformer models in various research fields including Natural Language Processing, Computational Vision as well as natural science territory. In the HEP community, models with transformer backbones have shown their power in jet tagging tasks. However, despite the impressive performance, transformer-based models are often large and computational heavily, resulting in low inference speeds. In this talk, I will discuss the preliminary results of our effort in accelerating the transformer models in the context of FastML.
Author
Sitian Qian
(Peking University (CN))
