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  1. Book ; Online: ML-based Real-Time Control at the Edge

    Shi, R. / Ogrenci, S. / Arnold, J. M. / Berlioz, J. R. / Hanlet, P. / Hazelwood, K. J. / Ibrahim, M. A. / Liu, H. / Nagaslaev, V. P. / 1, A. Narayanan / Nicklaus, D. J. / Mitrevski, J. / Pradhan, G. / Saewert, A. L. / Schupbach, B. A. / Seiya, K. / Thieme, M. / Thurman-Keup, R. M. / Tran, N. V.

    An Approach Using hls4ml

    2023  

    Abstract: This study focuses on implementing a real-time control system for a particle accelerator facility that performs high energy physics experiments. A critical operating parameter in this facility is beam loss, which is the fraction of particles deviating ... ...

    Abstract This study focuses on implementing a real-time control system for a particle accelerator facility that performs high energy physics experiments. A critical operating parameter in this facility is beam loss, which is the fraction of particles deviating from the accelerated proton beam into a cascade of secondary particles. Accelerators employ a large number of sensors to monitor beam loss. The data from these sensors is monitored by human operators who predict the relative contribution of different sub-systems to the beam loss. Using this information, they engage control interventions. In this paper, we present a controller to track this phenomenon in real-time using edge-Machine Learning (ML) and support control with low latency and high accuracy. We implemented this system on an Intel Arria 10 SoC. Optimizations at the algorithm, high-level synthesis, and interface levels to improve latency and resource usage are presented. Our design implements a neural network, which can predict the main source of beam loss (between two possible causes) at speeds up to 575 frames per second (fps) (average latency of 1.74 ms). The practical deployed system is required to operate at 320 fps, with a 3ms latency requirement, which has been met by our design successfully.
    Keywords Computer Science - Hardware Architecture
    Subject code 006
    Publishing date 2023-11-09
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  2. Book ; Online: Accelerator Real-time Edge AI for Distributed Systems (READS) Proposal

    Seiya, K. / Hazelwood, K. J. / Ibrahim, M. A. / Nagaslaev, V. P. / Nicklaus, D. J. / Schupbach, B. A. / Thurman-Keup, R. M. / Tran, N. V. / Liu, H. / Memik, S.

    2021  

    Abstract: Our objective will be to integrate ML into Fermilab accelerator operations and furthermore provide an accessible framework which can also be used by a broad range of other accelerator systems with dynamic tuning needs. We will develop of real-time ... ...

    Abstract Our objective will be to integrate ML into Fermilab accelerator operations and furthermore provide an accessible framework which can also be used by a broad range of other accelerator systems with dynamic tuning needs. We will develop of real-time accelerator control using embedded ML on-chip hardware and fast communication between distributed systems in this proposal. We will demonstrate this technology for the Mu2e experiment by increasing the overall duty factor and uptime of the experiment through two synergistic projects. First, we will use deep reinforcement learning techniques to improve the performance of the regulation loop through guided optimization to provide stable proton beams extracted from the Delivery Ring to the Mu2e experiment. This requires the development of a digital twin of the system to model the accelerator and develop real-time ML algorithms. Second, we will use de-blending techniques to disentangle and classify overlapping beam losses in the Main Injector and Recycler Ring to reduce overall beam downtime in each machine. This ML model will be deployed within a semi-autonomous operational mode. Both applications require processing at the millisecond scale and will share similar ML-in-hardware techniques and beam instrumentation readout technology. A collaboration between Fermilab and Northwestern University will pull together the talents and resources of accelerator physicists, beam instrumentation engineers, embedded system architects, FPGA board design experts, and ML experts to solve complex real-time accelerator controls challenges which will enhance the physics program. More broadly, the framework developed for Accelerator Real-time Edge AI Distributed Systems (READS) can be applied to future projects as the accelerator complex is upgraded for the PIP-II and DUNE era.
    Keywords Physics - Accelerator Physics
    Subject code 629
    Publishing date 2021-03-05
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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