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
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 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 your expertise/areas in which you would like to contribute / advise.
ANN training and VHDL implementation for FPGAs
Please describe areas in which you can contribute to “data handling” teaching.
Real-time processing, ANN developments
Please describe areas in which you would like to improve your knowledge / skills.
Ressource efficient and bit-exact ANN implementations in FPGAs
What is your field and role?
Particle Physics, ATLAS experiment, Upgrade of the readout electronics of the ATLAS LAr Calorimeters
| 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)
