Speaker
Description
Real-time data processing is a frontier field in experimental particle physics.
Machine learning methods are widely used and have proven highly effective in particle physics.
The increasing computing power of modern FPGAs allows for the addition of more sophisticated algorithms for real-time data processing.
Many tasks can be solved using modern machine learning (ML) algorithms, which are naturally suited to FPGA architectures.
An FPGA-based machine learning algorithm provides extremely low , sub-microsecond, decision latency, and makes information-rich datasets for event selection.
The project includes the development of a Machine Learning algorithm based on FPGAs for real-time particle identification and tracking in a Transition Radiation Detector and an Electromagnetic Calorimeter.
This report describes the progress in developing the ML-FPGA system and the results of beam tests.
| Minioral | No |
|---|---|
| IEEE Member | No |
| Are you a student? | No |