IBIC2022 - List of Authors (Santamaria Garcia, A.)

Paper	Title	Page
MOP42	KINGFISHER: A Framework for Fast Machine Learning Inference for Autonomous Accelerator Systems	151
	L. Scomparin, E. Blomley, E. Bründermann, M. Caselle, T. Dritschler, A. Kopmann, A. Mochihashi, A.-S. Müller, A. Santamaria Garcia, P. Schreiber, J.L. Steinmann, M. Weber KIT, Karlsruhe, Germany T. Boltz SLAC, Menlo Park, California, USA
	Modern particle accelerator facilities allow new and exciting beam properties and operation modes. Traditional real-time control systems, albeit powerful, have bandwidth and latency constraints that limit the range of operating conditions currently made available to users. The capability of Reinforcement Learning to perform self-learning control policies by interacting with the accelerator is intriguing. The extreme dynamic conditions require fast real-time feedback throughout the whole control loop from the diagnostic, with novel and intelligent detector systems, all the way to the interaction with the accelerator components. In this contribution, the novel KINGFISHER framework based on the modern Xilinx Versal devices will be presented. Versal combines several computational engines, specifically combining powerful FPGA logic with programmable AI Engines in a single device. Furthermore, this system can be natively integrated with the fastest beam diagnostic tools already available, i.e. KAPTURE and KALYPSO.
DOI •	reference for this paper ※ doi:10.18429/JACoW-IBIC2022-MOP42
About •	Received ※ 07 September 2022 — Revised ※ 09 September 2022 — Accepted ※ 12 September 2022 — Issue date ※ 09 October 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

KINGFISHER: A Framework for Fast Machine Learning Inference for Autonomous Accelerator Systems

151

L. Scomparin, E. Blomley, E. Bründermann, M. Caselle, T. Dritschler, A. Kopmann, A. Mochihashi, A.-S. Müller, A. Santamaria Garcia, P. Schreiber, J.L. Steinmann, M. Weber
KIT, Karlsruhe, Germany
T. Boltz
SLAC, Menlo Park, California, USA

Modern particle accelerator facilities allow new and exciting beam properties and operation modes. Traditional real-time control systems, albeit powerful, have bandwidth and latency constraints that limit the range of operating conditions currently made available to users. The capability of Reinforcement Learning to perform self-learning control policies by interacting with the accelerator is intriguing. The extreme dynamic conditions require fast real-time feedback throughout the whole control loop from the diagnostic, with novel and intelligent detector systems, all the way to the interaction with the accelerator components. In this contribution, the novel KINGFISHER framework based on the modern Xilinx Versal devices will be presented. Versal combines several computational engines, specifically combining powerful FPGA logic with programmable AI Engines in a single device. Furthermore, this system can be natively integrated with the fastest beam diagnostic tools already available, i.e. KAPTURE and KALYPSO.

DOI •

reference for this paper ※ doi:10.18429/JACoW-IBIC2022-MOP42

About •

Received ※ 07 September 2022 — Revised ※ 09 September 2022 — Accepted ※ 12 September 2022 — Issue date ※ 09 October 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)