Mar 5 – 8, 2024
Lahan Select Gyeongju, South Korea
Asia/Seoul timezone

Real-time Reinforcement Learning on FPGA with Online Training for Autonomous Accelerators

Mar 6, 2024, 11:30 AM
20m
Lahan Select Gyeongju, South Korea

Lahan Select Gyeongju, South Korea

Lahan Select Gyeongju, South Korea
Oral (16mins + 4 mins) Optimization & Control Optimization & Control

Speaker

Luca Scomparin (KIT)

Description

Reinforcement Learning (RL) is a promising approach for the autonomous AI-based control of particle accelerators. Real-time requirements for these algorithms can often not be satisfied with conventional hardware platforms.
In this contribution, the unique KINGFISHER platform being developed at KIT will be presented. Based on the novel AMD-Xilinx Versal platform, this system provides cutting-edge general microsecond-latency RL agents, specifically designed to perform online-training in a live environment.
The successful application of this system to dampen horizontal betatron oscillations at the KArlsruhe Research Accelerator (KARA) will be discussed. Additionally, preliminary results of the application of the system to the highly non-linear problem of controlling microbunching instabilities will be presented.

Primary Keyword reinforcement learning
Secondary Keyword AI-based controls

Primary author

Luca Scomparin (KIT)

Co-authors

Andrea Santamaria Garcia (Karlsruhe Institute of Technology) Dr Andreas Kopmann (KIT) Prof. Anke-Susanne Müller (KIT) Chenran Xu (Karlsruhe Institut für Technologie (KIT)) Dr Edmund Blomley (KIT) Dr Erik Bründermann (Karlsruhe Institute of Technology (KIT)) Dr Johannes L. Steinmann (KIT) Prof. Jürgen Becker (KIT) Dr Michele Caselle (KIT)

Presentation materials