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000910466 1001_ $$0P:(DE-Juel1)156247$$aFurletov, S.$$b0$$eCorresponding author
000910466 245__ $$aMachine learning on FPGA for event selection
000910466 260__ $$aLondon$$bInst. of Physics$$c2022
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000910466 520__ $$aReal-time data processing is a frontier field in experimental particle physics. The application of FPGAs at the trigger level is used by many current and planned experiments (CMS, LHCb, Belle2, PANDA). Usually they use conventional processing algorithms. LHCb has implemented Machine Learning (ML) elements for real-time data processing with a triggered readout system that runs most of the ML algorithms on a computer farm. The work described in this article aims to test the ML-FPGA algorithms for streaming data acquisition. There are many experiments working in this area and they have a lot in common, but there are many specific solutions for detector and accelerator parameters that are worth exploring further. This report describes the purpose of the work and progress in evaluating the ML-FPGA application.
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000910466 7001_ $$0P:(DE-HGF)0$$aBarbosa, F.$$b1
000910466 7001_ $$0P:(DE-HGF)0$$aBelfore, L.$$b2
000910466 7001_ $$0P:(DE-HGF)0$$aDickover, C.$$b3
000910466 7001_ $$0P:(DE-HGF)0$$aFanelli, C.$$b4
000910466 7001_ $$0P:(DE-HGF)0$$aFurletova, Y.$$b5
000910466 7001_ $$0P:(DE-Juel1)156472$$aJokhovets, L.$$b6
000910466 7001_ $$0P:(DE-HGF)0$$aLawrence, D.$$b7
000910466 7001_ $$0P:(DE-HGF)0$$aRomanov, D.$$b8
000910466 773__ $$0PERI:(DE-600)2235672-1$$a10.1088/1748-0221/17/06/C06009$$gVol. 17, no. 06, p. C06009 -$$n06$$pC06009$$tJournal of Instrumentation$$v17$$x1748-0221$$y2022
000910466 8564_ $$uhttps://juser.fz-juelich.de/record/910466/files/Furletov_2022_J._Inst._17_C06009.pdf
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