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

Entry from the 28.11.2019
Position number IPE 19-20
Stelle ist zu besetzen ab: offen/nach Absprache

Beschreibung

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.

Persönliche Qualifikation

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)

Organisationseinheit

Institut für Prozessdatenverarbeitung und Elektronik (IPE)

Contract Duration

limited regarding study regulations

Contact person in line-management

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


Zur Bewerbung Zur offiziellen Anzeige

Job type/category
  • Fixed-term position
Favored career stage
  • Job experience > 2 years
Location/region
  • Karlsruhe city, Karlsruhe region
Sector
  • Other
Language at workplace
  • German
Type of company
  • Wissenschaftliche Einrichtung


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