Hardware acceleration of deep learning models for particle tracking at the LHC and beyond

This project will be performed in collaboration with the Maastricht Science Programme and Nikhef

Objective

With the anticipated upgrade of the Large Hadron Collider (LHC), particle physics is entering the high-luminosity (HL) era of the accelerator. This brings new challenges to particle track reconstruction, not only due to the extreme particle multiplicities but also because of high pile-up rates (i.e. multiple independent proton-proton collisions occurring within the same time window). These conditions make the use of efficient models for particle tracking critical. Deep Learning (DL) approaches are especially promising, as they can reduce computational resource usage while maintaining or even improving physics performance.

How

You will focus on the deployment of optimized deep learning models for particle tracking on specialized hardware, such as GPUs and FPGAs. The goal of this thesis is to evaluate the trade-offs between speed, energy efficiency, and physics performance when running these models on accelerated platforms.

Outputs

Benchmark of DL models on different hardware backends such as GPU and FPGAs. Measurement of impact on throughput, power consumption and eventually physics performance (e.g. efficiency vs fake track rates). If the validation yields state of the art results and if time permits, the publication and presentation of the results in an international conference.

Prerequisites

1. Solid understanding of Machine Learning and Deep Learning fundamentals
2. Understanding of GPU or FPGA fundamentals
3. Programming skills in Python and/or C++