Speaker
Arno Straessner
(IKTP, TU Dresden)
Description
- Calorimeter data at current LHC experiments (e.g. ATLAS) require real-time energy reconstruction
- Signal pile-up (in-time and out-of-time) is a challenge
- ML approaches, like artificial neural networks (ANN), look promising
- ANN training/application needs to be resource efficient for FPGA implementation
- ANN training/application needs to be aware of bit precision for FPGA implementation
- VHDL, HLS and general tools shall be used/further developed to achieve the goal
What is your expertise in computing and / or software development?
Simulation of real-time data streams for ML training
In ErUM-Data, what kind of data are you dealing with?
Data streams from particle detector with high channel count and large data volume (250 Tb/s)
Please describe areas in which you would like to improve your knowledge / skills.
Ressource efficient and bit-exact ANN implementations in FPGAs
Please describe your expertise/areas in which you would like to contribute / advise.
ANN training and VHDL implementation for FPGAs
My current most burning research question, I like to find partners for, is:
ANN implementation for real-time processing of data sequences with FPGAs (INTEL) using VHDL / HLS and ML tools for FGPAs
What is your field and role?
Particle Physics, ATLAS experiment, Upgrade of the readout electronics of the ATLAS LAr Calorimeters
Please describe areas in which you can contribute to “data handling” teaching.
Real-time processing, ANN developments
| Your ErUM - Committee is | KET - Komitee für Elementarteilchenphysik |
|---|---|
| Do you consent to the data usage and public abstract data posting in the ErUM-Data Community Information Exchange? | Yes |
Primary author
Arno Straessner
(IKTP, TU Dresden)
