The Joint Accelerator Conferences Website (JACoW) is an international collaboration that publishes the proceedings of accelerator conferences held around the world.
TY - CONF AU - Shen, G. AU - Arnold, N.D. AU - Berenc, T.G. AU - Carwardine, J. AU - Chandler, E. AU - Fors, T. AU - Madden, T.J. AU - Paskvan, D.R. AU - Roehrig, C. AU - Shoaf, S.E. AU - Veseli, S. ED - Liu, Lin ED - Byrd, John M. ED - Neuenschwander, Regis T. ED - Picoreti, Renan ED - Schaa, Volker R. W. TI - High Performance DAQ Infrastructure to Enable Machine Learning for the Advanced Photon Source Upgrade 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 - It is well known that the efficiency of an advanced control algorithm like machine learning is as good as its data quality. Much recent progress in technology enables the massive data acquisition from a control system of modern particle accelerator, and the wide use of embedded controllers, like field-programmable gate arrays (FPGA), provides an opportunity to collect fast data from technical subsystems for monitoring, statistics, diagnostics or fault recording. To improve the data quality, at the APS Upgrade project, a general-purpose data acquisition (DAQ) system is under active development. The APS-U DAQ system collects high-quality fast data from underneath embedded controllers, especially the FPGAs, with the manner of time-correlation and synchronously sampling, which could be used for commissioning, performance monitoring, troubleshooting, and early fault detection, etc. This paper presents the design and latest progress of APS-U high-performance DAQ infrastructure, as well as its preparation to enable the use of machine learning technology for APS-U, and its use cases at APS. PB - JACoW Publishing CP - Geneva, Switzerland SP - 3434 EP - 3436 KW - monitoring KW - controls KW - EPICS KW - data-acquisition KW - hardware DA - 2021/08 PY - 2021 SN - 2673-5490 SN - 978-3-95450-214-1 DO - doi:10.18429/JACoW-IPAC2021-WEPAB323 UR - https://jacow.org/ipac2021/papers/wepab323.pdf ER -