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Towards Intelligent Data Acquisition Systems with Embedded Deep Learning on MPSoC

Wang, Weijia

Abstract (englisch):
Large-scale scientific experiments rely on dedicated high-performance data-acquisition systems to sample, readout, analyse, and store experimental data. However, with the rapid development in detector technology in various fields, the number of channels and the data rate are increasing. For trigger and control tasks data acquisition systems needs to satisfy real-time constraints, enable short-time latency and provide the possibility to integrate intelligent data processing. During recent years machine learning approaches have been used successfully in many applications. This dissertation will study how machine learning techniques can be integrated already in the data acquisition of large-scale experiments. A universal data acquisition platform for multiple data channels has been developed. Different machine learning implementation methods and application have been realized using this system.



On the hardware side, recent FPGAs do not only provide high-performance parallel logic but more and more additional features, like ultra-fast transceivers and embedded ARM processors. TSMC's 16nm FinFET Plus (16FF+) 3D transistor technology enables Xilinx in the Zynq UltraScale+ FPGA devices to increase the performance/watt ratio by 2 to 5 times compared to their previous generation. ... mehr


Volltext §
DOI: 10.5445/IR/1000133898
Veröffentlicht am 24.06.2021
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Prozessdatenverarbeitung und Elektronik (IPE)
Publikationstyp Hochschulschrift
Publikationsdatum 24.06.2021
Sprache Englisch
Identifikator KITopen-ID: 1000133898
HGF-Programm 54.12.02 (POF IV, LK 01) System Technologies
Verlag Karlsruher Institut für Technologie (KIT)
Umfang viii, 166 S.
Art der Arbeit Dissertation
Fakultät Fakultät für Elektrotechnik und Informationstechnik (ETIT)
Institut Institut für Prozessdatenverarbeitung und Elektronik (IPE)
Prüfungsdatum 21.12.2020
Referent/Betreuer Weber, M.
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