Decoding Decay - Real-Time Particle Detection from Point Clouds

Context

Dive into the heart of particle physics with Belle II located in Tsukuba, Japan. In our quest for indirect proof of dark matter and other rare decays, we are thriving to improve the efficiency of the detector through point cloud neural networks. Recently, these algorithms have shown promising performance in classic computer vision applications.

In this thesis, you will find out what it takes to adapt point cloud algorithms for online processing in large scientific experiments such as Belle II. You will have the opportunity to get in touch with FPGA firmware development, embedded system integration, and algorithm hardware co-design for high-performance computing systems.

Targets

You will work with us to improve the performance of our state-of-the-art point cloud network accelerator using top-of-the-line FPGAs and CGRAs through innovative digital design automation methods. Together, we will bring real-time machine learning on FPGAs for Particle Physics one step close to reality.

Requirements 

• We're looking for enthusiastic students who are keen to learn and grow with us.
• Ideally, you've taken one of these courses: Laboratory System-on-Chip (PSoC), Design Hardware Laboratory (DHL), Hardware Synthesis and Optimization (HSO), Hardware Software Co-Design (HSC), Digital Circuit Design (DDS).
• Experience with FPGA firmware development is great, but not a must-have.
• You should enjoy working in a diverse, international, and interdisciplinary team.
• You need to be fluent in either German or English.