% IMPORTANT: The following is UTF-8 encoded. This means that in the presence % of non-ASCII characters, it will not work with BibTeX 0.99 or older. % Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or % “biber”. @ARTICLE{Furletov:910466, author = {Furletov, S. and Barbosa, F. and Belfore, L. and Dickover, C. and Fanelli, C. and Furletova, Y. and Jokhovets, L. and Lawrence, D. and Romanov, D.}, title = {{M}achine learning on {FPGA} for event selection}, journal = {Journal of Instrumentation}, volume = {17}, number = {06}, issn = {1748-0221}, address = {London}, publisher = {Inst. of Physics}, reportid = {FZJ-2022-03849}, pages = {C06009}, year = {2022}, note = {Post-print leider nicht verfügbar}, abstract = {Real-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.}, cin = {ZEA-2}, ddc = {610}, cid = {I:(DE-Juel1)ZEA-2-20090406}, pnm = {622 - Detector Technologies and Systems (POF4-622)}, pid = {G:(DE-HGF)POF4-622}, typ = {PUB:(DE-HGF)16}, UT = {WOS:000823617800002}, doi = {10.1088/1748-0221/17/06/C06009}, url = {https://juser.fz-juelich.de/record/910466}, }