IPE 19-20 Bachelor- or Masterthesis: Development of FPGA-based machine learning algorithms for applications in high-energy physics
Institute for Data Processing and Electronics (IPE)
Machine learning builds on ideas in computer science, statistics, and optimization. It aims to develop algorithms to identify patterns and regularities in data, and use these learned patterns to make predictions for future observations. Boosted by successful industrial and commercial applications the field of machine learning is quickly expanding. In this thesis you should apply recent machine learning approaches, such as deep learning, to the analysis of data from high-energy physics experiments. You will use Xilinx’s Zynq SoC/MPSoC, an ideal platform for machine learning applications, and Xilinx’s reVISION Stack. It removes traditional design barriers by allowing you to quickly take a trained network and deploy it on Zynq SoC and MPSoC for inference.
as soon as possible
- Study the Xilinx Zynq FPGA platform and its development tools
- Develop a test bench of machine-learning algorithms
- Design a workflow to implement machine-learning algorithms in the Xilinx reVISION framework
- Knowledge in C and Verilog/VHDL (basic)
- Embedded and hardware programming (better but not required)
- Previous experience with developing for a Xilinx Zynq SoC (better but not required)
limited regarding study regulations
Contact person in line-management
Dr. Ing. Michele Caselle (IPE) (0721 / 608 25903), email: firstname.lastname@example.org
Please apply online using the button below for this vacancy number IPE 19-20.
Ausschreibungsnummer: IPE 19-20
Recognized severely disabled persons will be preferred if they are equally qualified.
Personnel Support is provided by:
Phone: +49 721 608-25184,
Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany