About
**Project Description**:
The project studies how a jet tagging model with a transformer architecture trained on synthetic top quark pair production events can be compressed with [PQuantML][1], a library for hardware-aware compression of neural networks during training, for inference on compute-constrained devices such as FPGAs, which are used for ultra-low-latency classification tasks such as jet flavour tagging.
**Supervisor**: Roope Niemi
[1]: https://pquantml.readthedocs.io/en/latest/ "PQuantML"
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The talk
Credits and licence
- Institution
- CERN
- Speaker
- Dino Liwen Cheng
- Year
- 2026
- Language
- English
- Source
- CERN
- Licence
- CERN, as CERN states it. CERN's terms
- Attribution
- © CERN. CERN provides its audiovisual media free of charge for educational and informational use, credited to CERN; this lecture is shown in CERN's own player.
- Who can watch
- CERN gives no rating. Invisible Network shows it to Teens profiles and older, judged from what the source says about it. How courses are placed