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4th ICFA Beam Dynamics Mini-Workshop on Machine Learning Applications for Particle Accelerators

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

Session

Infrastructure / Deployment Workflows

Mar 6, 2024, 11:50 AM
Lahan Select Gyeongju, South Korea

Lahan Select Gyeongju, South Korea

Lahan Select Gyeongju, South Korea

Conveners

Infrastructure / Deployment Workflows: Infrastructure 1

  • Verena Kain (CERN)
  • Myunghoon Cho (Pohang Accelerator Laboratory)

Infrastructure / Deployment Workflows: Infrastructure 2

  • Myunghoon Cho (Pohang Accelerator Laboratory)
  • Verena Kain (CERN)

Presentation materials

There are no materials yet.

  1. 52. Leveraging Vendor Tools for AI Acceleration
    Jonathan Edelen (RadiaSoft LLC), Joshua Einstein-Curtis (RadiaSoft LLC)
    3/6/24, 11:50 AM
    Infrastructure / Deployment Workflows
    Oral (16mins + 4 mins)

    Several large vendors have been expanding their ML deployment tooling to allow for easy deployment of machine learning models on processing devices. AMD Xilinx has developed a toolkit for accelerating ML calculations on their FPGAs by utilizing either dedicated “AI Engine” (AIE) hardware or an openly available IP block known as “Deep Learning Processing Units” (DPUs). Vitis AI is actively...

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  2. 4. An Open Source MLOps Pipeline for Particle Accelerators
    Gopika Bhardwaj (Fermilab)
    3/6/24, 12:10 PM
    Infrastructure / Deployment Workflows
    Oral (16mins + 4 mins)

    In this talk we will describe the open-source MLOps (Machine Learning Operations) framework tools selected and designed to support ML (Machine Learning) algorithm development and deployment on Fermilab’s main accelerator complex. MLOps is the standardization and streamlining of the ML development lifecycle to address the challenges and risks associated with large-scale machine learning...

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  3. 62. Lean MLOps stack for development and deployment of Machine Learning models into an EPICS Control system
    Mateusz Leputa (UKRI-STFC-ISIS)
    3/6/24, 1:30 PM
    Infrastructure / Deployment Workflows
    Oral (16mins + 4 mins)

    The ISIS Neutron and Muon Source is undergoing several upgrades to the control hardware, software, data acquisition and archiving systems. Machine learning systems are also being integrated into the control system. This not only requires the models to be high-quality but also to be maintained and kept up to date, especially in performance-critical applications. Each model incurs additional...

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  4. 29. Building a FAIR-Compliant Platform for AI-Ready Data in Particle Accelerators
    Xiaohan Lu
    3/6/24, 1:50 PM
    Infrastructure / Deployment Workflows
    Oral (16mins + 4 mins)

    In the rapidly evolving field of particle accelerator technology, Machine Learning (ML) shows great potential in optimizing accelerator performance and predictive maintenance. However, the success of these applications often depends on high-quality, real-time data sources. This paper introduces the plan and status of building an innovative machine learning data acquisition platform,...

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  5. 8. Automation CERN – Progress with automating CERN’s accelerator fleet
    Michael Schenk (CERN)
    3/6/24, 2:10 PM
    Infrastructure / Deployment Workflows
    Oral (16mins + 4 mins)

    Automation has become one of the key topics of preparing CERN’s accelerators for the future. It has been identified as essential for the existing complex to cope with upcoming challenges as well as for future projects such as the FCC, that will require a significant reduction of exploitation cost compared to today’s standards to get accepted. In recent years, the highest impact areas of...

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  6. 55. Interpretable Machine Learning at the European XFEL
    Danilo Enoque Ferreira de Lima (European X-ray Free Electron Laser)
    3/6/24, 2:30 PM
    Infrastructure / Deployment Workflows
    Oral (16mins + 4 mins)

    The success of experiments at large scale photon sources is strongly connected with the quality of collected data and the information available to scientists during the beamtime. Similarly, streamlined and automated operation of the facility can minimize inefficiencies, thereby boosting the scientific outcome.

    The key strategic goal of the Machine Learning program at the European XFEL is...

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  7. 60. Real-time Integration of Machine Learning for Beam Size Control at the Advanced Light Source
    Thorsten Hellert (Lawrence Berkeley National Laboratory)
    3/6/24, 2:50 PM
    Infrastructure / Deployment Workflows
    Oral (16mins + 4 mins)

    The Advanced Light Source (ALS) storage ring employs various feedback and feedforward systems to stabilize the circulating electron beam thus ensuring delivery of steady synchrotron radiation to the users.

    In particular, active correction is essential to compensate for the significant perturbations to the transverse beam size induced by user-controlled tuning of the insertion devices,...

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  8. 45. A Front-End Framework with Embedded ML Tools for Automating Neutron Scattering Experiments
    Morgan Henderson (RadiaSoft LLC)
    3/6/24, 3:10 PM
    Infrastructure / Deployment Workflows
    Oral (16mins + 4 mins)

    The rscontrols framework developed at RadiaSoft was created to simplify controls automation for neutron scattering experiments using machine learning (ML), beginning with sample alignment. Written in Python, rscontrols uses virtual representations of equipment and controls to enable seamless integration of hardware, EPICS protocols, and analytical tools including deep networks and other ML...

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