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RIS citation export for TU3C02: FPGA Architectures for Distributed ML Systems for Real-Time Beam Loss De-Blending

TY  - CONF
AU  - Ibrahim, M.A.
AU  - Arnold, J.M.S.
AU  - Austin, M.R.
AU  - Berlioz, J.R.
AU  - Hanlet, P.M.
AU  - Hazelwood, K.J.
AU  - Hu, J.YC.
AU  - Jiang, J.
AU  - Liu, H.
AU  - Memik, S.
AU  - Mitrevski, J.
AU  - Nagaslaev, V.P.
AU  - Narayanan, A.
AU  - Nicklaus, D.J.
AU  - Pradhan, G.
AU  - Saewert, A.L.
AU  - Schupbach, B.A.
AU  - Seiya, K.
AU  - Shi, R.
AU  - Shuping, A.M.
AU  - Thieme, M.
AU  - Thurman-Keup, R.M.
AU  - Tran, N.V.
AU  - Xu, C.
ED  - Schaa, Volker R.W.
ED  - Batten, Tonia
ED  - Bree, Michael
ED  - Petit-Jean-Genaz, Christine
ED  - Hunter, Darren
TI  - FPGA Architectures for Distributed ML Systems for Real-Time Beam Loss De-Blending
J2  - Proc. of IBIC2023, Saskatoon, Canada, 10-14 September 2023
CY  - Saskatoon, Canada
T2  - International Beam Instrumentation Conference
T3  - 12
LA  - english
AB  - The Real-time Edge AI for Distributed Systems (READS) project’s goal is to create a Machine Learning (ML) system for real-time beam loss de-blending within the accelerator enclosure, which houses two accelerators: the Main Injector (MI) and the Recycler (RR). In periods of joint operation, when both machines contain high intensity beam, radiative beam losses from MI and RR overlap on the enclosure¿s beam loss monitoring (BLM) system, making it difficult to attribute those losses to a single machine. Incorrect diagnoses result in unnecessary downtime that incurs both financial and experimental cost. The ML system will automatically disentangle each machine¿s contributions to those measured losses, while not disrupting the existing operations-critical functions of the BLM system. Within this paper, the ML models, used for learning both local and global machine signatures and producing high quality inferences based on raw BLM loss measurements, will only be discussed at a high-level. This paper will focus on the evolution of the architecture, which provided the high-frequency, low-latency collection of synchronized data streams to make real-time inferences.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 160
EP  - 163
KW  - network
KW  - real-time
KW  - distributed
KW  - operation
KW  - FPGA
DA  - 2023/12
PY  - 2023
SN  - 2673-5350
SN  - 978-3-95450-236-3
DO  - doi:10.18429/JACoW-IBIC2023-TU3C02
UR  - https://jacow.org/ibic2023/papers/tu3c02.pdf
ER  -