The Joint Accelerator Conferences Website (JACoW) is an international collaboration that publishes the proceedings of accelerator conferences held around the world.
@inproceedings{shen:ipac2021-wepab323,
author = {G. Shen and N.D. Arnold and T.G. Berenc and J. Carwardine and E. Chandler and T. Fors and T.J. Madden and D.R. Paskvan and C. Roehrig and S.E. Shoaf and S. Veseli},
% author = {G. Shen and N.D. Arnold and T.G. Berenc and J. Carwardine and E. Chandler and T. Fors and others},
% author = {G. Shen and others},
title = {{High Performance DAQ Infrastructure to Enable Machine Learning for the Advanced Photon Source Upgrade}},
booktitle = {Proc. IPAC'21},
pages = {3434--3436},
eid = {WEPAB323},
language = {english},
keywords = {monitoring, controls, EPICS, data-acquisition, hardware},
venue = {Campinas, SP, Brazil},
series = {International Particle Accelerator Conference},
number = {12},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {08},
year = {2021},
issn = {2673-5490},
isbn = {978-3-95450-214-1},
doi = {10.18429/JACoW-IPAC2021-WEPAB323},
url = {https://jacow.org/ipac2021/papers/wepab323.pdf},
note = {https://doi.org/10.18429/JACoW-IPAC2021-WEPAB323},
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.}},
}