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
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)
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
In ErUM-Data, what kind of data are you dealing with?
Raw data from the ASICs of the detector
Please describe your expertise/areas in which you would like to contribute / advise.
DAQ systems and design, FPGA programming
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 can contribute to “data handling” teaching.
DAQ systems, data driven, self-triggered readout
| 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)
