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Analysis of institutional authors

Astrain MCorresponding AuthorRuiz MCorresponding AuthorCarpeño AAuthorEsquembri SAuthorRivilla DAuthor
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Development of deep learning applications in FPGA-based fusion diagnostics using IRIO-OpenCL and NDS

Publicated to:Fusion Engineering And Design. 168 112393- - 2021-07-01 168(), DOI: 10.1016/j.fusengdes.2021.112393

Authors: Astrain M; Ruiz M; Carpeño A; Esquembri S; Rivilla D

Affiliations

Univ Politecnica Madrid, Instrumentat & Appl Acoust Res Grp, Madrid, Spain - Author
Universidad Politécnica de Madrid - Author

Abstract

© 2021 Elsevier B.V. Many of the signals that are relevant to fusion science come from 1D signals or time-series. In this field, the resulting Neural Networks are much simpler than the more mainstream vision-based neural networks. A significant reduction in both dimension and complexity make them suitable to be synthesized in FPGAs. We have developed new features for the IRIO-OpenCL platform to support this technology for fusion problems. The work presented analyzes the feasibility of such diagnostics use cases and how they can be integrated with the help of OpenCL technology. The development and testing platform consists of an MTCA.4 system with an AMC module integrating an Intel Arria 10 FPGA. An ADC connected using the FMC interface samples the analog signals passed to the OpenCL processing kernels. By using OpenCL, the FPGA kernels communicate with the host machine in a standardized way. This brings two main advantages. First, this is an ideal prototyping framework. Second, once a solution is final, the FPGA kernels are integrated into the control system (EPICS) using the IRIO-OpenCL layer, which has been developed with Nominal Device Support (NDSv3). Finally, we present the results of the optimizations to the kernels of an application example based on a neutron/gamma discrimination Neural Network, which achieves up to a classification rate of 1.3 MEvents/s.

Keywords
codacdaqfpgamachine learningopenclCodacDaqFpgaMachine learningMtcaOpenclPulse-shape discrimination

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Fusion Engineering And Design due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2021, it was in position 11/34, thus managing to position itself as a Q2 (Segundo Cuartil), in the category Nuclear Science & Technology. Notably, the journal is positioned en el Cuartil Q2 para la agencia Scopus (SJR) en la categoría Mechanical Engineering.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2025-05-16:

  • Google Scholar: 3
  • Scopus: 5
  • OpenCitations: 2
Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-05-16:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 12 (PlumX).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (ASTRAIN ETXEZARRETA, MIGUEL) and Last Author (RIVILLA BRAVO, DANIEL).

the authors responsible for correspondence tasks have been ASTRAIN ETXEZARRETA, MIGUEL and RUIZ GONZALEZ, MARIANO.