Speaker
Michael Lupberger
(University of Bonn)
Description
- Future LHC and other experiments will procude more data than can be handled with current technology (spatial resolution, additional precise timing information, increased sensor size =>larger output bandwidth of new generation frontend chips)
- Same issue in many sectors of information driven society: Data explosion
- One solution: shift methods currently used in online and offline data processing to an earlier stage in DAQ chain => smart data: transmitting data properties as e.g. cluster or track parameters
- Currently: dedicated feature extraction algorithms (e.g. hough transformation) implemented on FPGAs, ML methods recently applied in triggering
- Problem: application of more advanced ML methods to reconstruct feature properties as they are used in online computer farms on CPUs and GPUs so far hindered by a lack of FPGA resources
- NEW: FPGA vendors are currently including dedicated AI cores in addition to FPGA and CPU resources (System on Chip)
=> This project: evaluate CPU+FPGA+AI devices (cross-disciplinary interest)
- Tansfere existing ML methods of computer science to hardware (high level synthesis tools & possibly more efficient implementation in hardware description language)
- Use data from latest frontend ASICs in high rate experiments R&D for qualification
- Additional topics:
-- reduction of power consumption for computing
--sociological: data protection by requested feature extraction within the acquisition
--philosophical: trustworthy AI
Please describe areas in which you can contribute to “data handling” teaching.
DAQ systems, data driven, self-triggered readout
My current most burning research question, I like to find partners for, is:
Use ML methods on FPGAs to face transition from big data to smart data (online, low latency)
What is your field and role?
Cross disciplinary, wherever DAQs are needed. Original field: experimental particle and hadron physics, detector development and instrumentation. Role: Marie-Curie Fellow and BMBF R&D co-project leader
Please describe areas in which you would like to improve your knowledge / skills.
Software ML tools, latest Xilinx hardwar ML tools
What is your expertise in computing and / or software development?
Hardware descruption language, understanding of high performance, low latency hardware (FPGAs), FPGA+CPU+AI devices are also interesting for computing centers to accelerate ML applications
Please describe your expertise/areas in which you would like to contribute / advise.
DAQ systems and design, FPGA programming
In ErUM-Data, what kind of data are you dealing with?
Raw data from the ASICs of the detector
| Your ErUM - Committee is | More than one |
|---|---|
| List of Committees: | KET, KHuK |
| Do you consent to the data usage and public abstract data posting in the ErUM-Data Community Information Exchange? | Yes |
Primary author
Michael Lupberger
(University of Bonn)
