| Title |
High Performance DAQ Infrastructure to Enable Machine Learning for the Advanced Photon Source Upgrade |
| Authors |
- G. Shen, N.D. Arnold, T.G. Berenc, J. Carwardine, E. Chandler, T. Fors, T.J. Madden, D.R. Paskvan, C. Roehrig, S.E. Shoaf, S. Veseli
ANL, Lemont, Illinois, USA
|
| Abstract |
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.
|
| Funding |
Argonne National Laboratory’s work was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under contract DE-AC02-06CH11357. |
| Paper |
download WEPAB323.PDF [0.746 MB / 3 pages] |
| Export |
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| Conference |
IPAC2021 |
| Series |
International Particle Accelerator Conference (12th) |
| Location |
Campinas, SP, Brazil |
| Date |
24-28 May 2021 |
| Publisher |
JACoW Publishing, Geneva, Switzerland |
| Editorial Board |
Liu Lin (LNLS, Campinas, Brazil); John M. Byrd (ANL, Lemont, IL, USA); Regis Neuenschwander (LNLS, Campinas, Brazil); Renan Picoreti (LNLS, Campinas, Brazil); Volker R. W. Schaa (GSI, Darmstadt, Germany) |
| Online ISBN |
978-3-95450-214-1 |
| Online ISSN |
2673-5490 |
| Received |
19 May 2021 |
| Accepted |
24 June 2021 |
| Issue Date |
29 August 2021 |
| DOI |
doi:10.18429/JACoW-IPAC2021-WEPAB323 |
| Pages |
3434-3436 |
| Copyright |
 Published by JACoW Publishing under the terms of the Creative Commons Attribution 3.0 International license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI. |
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