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QONNX: Representing Arbitrary-Precision Quantized Neural Networks / Pappalardo, Alessandro (Unlisted, IE) ; Umuroglu, Yaman (Unlisted, IE) ; Blott, Michaela (Unlisted, IE) ; Mitrevski, Jovan (Fermilab) ; Hawks, Ben (Fermilab) ; Tran, Nhan (Fermilab) ; Loncar, Vladimir (MIT, LNS) ; Summers, Sioni (CERN) ; Borras, Hendrik (U. Heidelberg) ; Muhizi, Jules (Harvard U. (main)) et al.
We present extensions to the Open Neural Network Exchange (ONNX) intermediate representation format to represent arbitrary-precision quantized neural networks. [...]
arXiv:2206.07527 ; FERMILAB-CONF-22-471-SCD. - 9.
Fermilab Library Server - Fulltext - Fulltext

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Generalized Machine Learning Quantization Implementation for High Level Synthesis Targeting FPGAs / Trahms, Matthew Karl
The Large Hadron Collider produces a large amount of data while operating, approximately one petabyte of data per second [...]
CERN-THESIS-2022-019 - 45.

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FPGA-accelerated machine learning inference as a service for particle physics computing / Duarte, Javier (Fermilab) ; Harris, Philip (MIT) ; Hauck, Scott (Washington U., Seattle) ; Holzman, Burt (Fermilab) ; Hsu, Shih-Chieh (Washington U., Seattle) ; Jindariani, Sergo (Fermilab) ; Khan, Suffian (Microsoft, Redmond) ; Kreis, Benjamin (Fermilab) ; Lee, Brian (Microsoft, Redmond) ; Liu, Mia (Fermilab) et al.
New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable Gate Arrays (FPGAs), offer exciting solutions with large potential gains. The growing applications of machine learning algorithms in particle physics for simulation, reconstruction, and analysis are naturally deployed on such platforms. [...]
arXiv:1904.08986; FERMILAB-PUB-19-170-CD-CMS-E-ND.- 2019-10-14 - 16 p. - Published in : Comput. Softw. Big Sci. 3 (2019) 13 Fulltext: 1904.08986 - PDF; fermilab-pub-19-170-cd-cms-e-nd - PDF; Fulltext from Publisher: PDF; External link: Fermilab Accepted Manuscript

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