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PE 19-20 Bachelor- or Masterthesis: Development of FPGA-based machine learning algorithms for applications in high-energy physics

Organizational unit

Institute for Data Processing and Electronics (IPE)

Job description

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.

Starting date

as soon as possible

Personal qualification

Your tasks:

  • 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

Your skills:

  • 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)

Contract Duration

limited regarding study regulations

Contact person in line-management

Dr. Ing. Michele Caselle (IPE) (0721 / 608 25903), email: michele.caselle@kit.edu

Please apply online using the button below for this vacancy number IPE 19-20.

Ausschreibungsnummer: IPE 19-20

If qualified, severely disabled persons will be preferred.

Contact

Personnel Support is provided by:
Ms Schaber
Phone: +49 721 608-25184,

Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany