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TY - CONF AU - Hazelwood, K.J. AU - Austin, M.R. AU - Ibrahim, M.A. AU - Liu, H. AU - Memik, S. AU - Nagaslaev, V.P. AU - Narayanan, A. AU - Nicklaus, D.J. AU - Saewert, A.L. AU - Schupbach, B.A. AU - Seiya, K. AU - Shi, R. AU - Thieme, M. AU - Thurman-Keup, R.M. AU - Tran, N.V. ED - Liu, Lin ED - Byrd, John M. ED - Neuenschwander, Regis T. ED - Picoreti, Renan ED - Schaa, Volker R. W. TI - Real-Time Edge AI for Distributed Systems (READS): Progress on Beam Loss De-Blending for the Fermilab Main Injector and Recycler J2 - Proc. of IPAC2021, Campinas, SP, Brazil, 24-28 May 2021 CY - Campinas, SP, Brazil T2 - International Particle Accelerator Conference T3 - 12 LA - english AB - The Fermilab Main Injector enclosure houses two accelerators, the Main Injector and Recycler. During normal operation, high intensity proton beams exist simultaneously in both. The two accelerators share the same beam loss monitors (BLM) and monitoring system. Beam losses in the Main Injector enclosure are monitored for tuning the accelerators and machine protection. Losses are currently attributed to a specific machine based on timing. However, this method alone is insufficient and often inaccurate, resulting in more difficult machine tuning and unnecessary machine downtime. Machine experts can often distinguish the correct source of beam loss. This suggests a machine learning (ML) model may be producible to help de-blend losses between machines. Work is underway as part of the Fermilab Real-time Edge AI for Distributed Systems Project (READS) to develop a ML empowered system that collects streamed BLM data and additional machine readings to infer in real-time, which machine generated beam loss. PB - JACoW Publishing CP - Geneva, Switzerland SP - 912 EP - 915 KW - network KW - real-time KW - operation KW - distributed KW - FPGA DA - 2021/08 PY - 2021 SN - 2673-5490 SN - 978-3-95450-214-1 DO - doi:10.18429/JACoW-IPAC2021-MOPAB288 UR - https://jacow.org/ipac2021/papers/mopab288.pdf ER -