Skip to main content

CORRECTION article

Front. Big Data, 16 October 2023
Sec. Big Data and AI in High Energy Physics
Volume 6 - 2023 | https://doi.org/10.3389/fdata.2023.1301942

Corrigendum: Applications and techniques for fast machine learning in science

This article is a correction to:

  1. Applications and Techniques for Fast Machine Learning in Science

    1. Read original article
\r\nAllison McCarn Deiana Allison McCarn Deiana1*Nhan Tran, Nhan Tran2,3*Joshua AgarJoshua Agar4Michaela BlottMichaela Blott5Giuseppe Di GuglielmoGiuseppe Di Guglielmo6Javier DuarteJavier Duarte7Philip HarrisPhilip Harris8Scott HauckScott Hauck9Mia LiuMia Liu10Mark S. NeubauerMark S. Neubauer11Jennifer NgadiubaJennifer Ngadiuba2Seda Ogrenci-MemikSeda Ogrenci-Memik3Maurizio PieriniMaurizio Pierini12Thea AarrestadThea Aarrestad12Steffen BhrSteffen Bähr13Jürgen BeckerJürgen Becker13Anne-Sophie BertholdAnne-Sophie Berthold14Richard J. BonventreRichard J. Bonventre15Toms E. Müller BravoTomás E. Müller Bravo16Markus DiefenthalerMarkus Diefenthaler17Zhen DongZhen Dong18Nick FritzscheNick Fritzsche14Amir GholamiAmir Gholami18Ekaterina GovorkovaEkaterina Govorkova12Dongning GuoDongning Guo3Kyle J. HazelwoodKyle J. Hazelwood2Christian HerwigChristian Herwig2Babar KhanBabar Khan19Sehoon KimSehoon Kim18Thomas KlijnsmaThomas Klijnsma2Yaling LiuYaling Liu20Kin Ho LoKin Ho Lo21Tri NguyenTri Nguyen8Gianantonio PezzulloGianantonio Pezzullo22Seyedramin RasoulinezhadSeyedramin Rasoulinezhad23Ryan A. RiveraRyan A. Rivera2Kate ScholbergKate Scholberg24Justin SeligJustin Selig25Sougata SenSougata Sen26Dmitri StrukovDmitri Strukov27William TangWilliam Tang28Savannah ThaisSavannah Thais28Kai Lukas UngerKai Lukas Unger13Ricardo VilaltaRicardo Vilalta29Belina von Krosigk,Belina von Krosigk13,30Shen WangShen Wang20Thomas K. WarburtonThomas K. Warburton31
  • 1Department of Physics, Southern Methodist University, Dallas, TX, United States
  • 2Fermi National Accelerator Laboratory, Batavia, IL, United States
  • 3Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States
  • 4Department of Materials Science and Engineering, Lehigh University, Bethlehem, PA, United States
  • 5Xilinx Research, Dublin, Ireland
  • 6Department of Computer Science, Columbia University, New York, NY, United States
  • 7Department of Physics, University of California, San Diego, San Diego, CA, United States
  • 8Massachusetts Institute of Technology, Cambridge, MA, United States
  • 9Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
  • 10Department of Physics and Astronomy, Purdue University, West Lafayette, IN, United States
  • 11Department of Physics, University of Illinois Urbana-Champaign, Champaign, IL, United States
  • 12European Organization for Nuclear Research (CERN), Meyrin, Switzerland
  • 13Karlsruhe Institute of Technology, Karlsruhe, Germany
  • 14Institute of Nuclear and Particle Physics, Technische Universität Dresden, Dresden, Germany
  • 15Lawrence Berkeley National Laboratory, Berkeley, CA, United States
  • 16Department of Physics and Astronomy, University of Southampton, Southampton, United Kingdom
  • 17Thomas Jefferson National Accelerator Facility, Newport News, VA, United States
  • 18Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States
  • 19Department of Computer Science, Technical University Darmstadt, Darmstadt, Germany
  • 20Department of Bioengineering, Lehigh University, Bethlehem, PA, United States
  • 21Department of Physics, University of Florida, Gainesville, FL, United States
  • 22Department of Physics, Yale University, New Haven, CT, United States
  • 23Department of Engineering and IT, University of Sydney, Camperdown, NSW, Australia
  • 24Department of Physics, Duke University, Durham, NC, United States
  • 25Cerebras Systems, Sunnyvale, CA, United States
  • 26Birla Institute of Technology and Science, Pilani, India
  • 27Department of Electrical and Computer Engineering, University of California, Santa Barbara, Santa Barbara, CA, United States
  • 28Department of Physics, Princeton University, Princeton, NJ, United States
  • 29Department of Computer Science, University of Houston, Houston, TX, United States
  • 30Department of Physics, Universität Hamburg, Hamburg, Germany
  • 31Department of Physics and Astronomy, Iowa State University, Ames, IA, United States

A corrigendum on
Applications and techniques for fast machine learning in science

by Deiana, A. M., Tran, N., Agar, J., Blott, M., Di Guglielmo, G., Duarte, J., Harris, P., Hauck, S., Liu, M., Neubauer, M. S., Ngadiuba, J., Ogrenci-Memik, S., Pierini, M., Aarrestad, T., Bähr, S., Becker, J., Berthold, A.-S., Bonventre, R. J., Müller Bravo, T. E., Diefenthaler, M., Dong, Z., Fritzsche, N., Gholami, A., Govorkova, E., Guo, D., Hazelwood, K. J., Herwig, C., Khan, B., Kim, S., Klijnsma, T., Liu, Y., Lo, K. H., Nguyen, T., Pezzullo, G., Rasoulinezhad, S., Rivera, R. A., Scholberg, K., Selig, J., Sen, S., Strukov, D., Tang, W., Thais, S., Unger, K. L., Vilalta, R., von Krosigk, B., Wang, S., and Warburton, T. K. (2022). Front. Big Data 5:787421. doi: 10.3389/fdata.2022.787421

In the published article, there was an error regarding the affiliation for author Anne-Sophie Berthold. Instead of having affiliation 25 (Cerebras Systems, Sunnyvale, CA, United States) they should have 14 (Institute of Nuclear and Particle Physics, Technische Universität Dresden, Dresden, Germany).

The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Keywords: machine learning for science, big data, particle physics, codesign, coprocessors, heterogeneous computing, fast machine learning

Citation: Deiana AM, Tran N, Agar J, Blott M, Di Guglielmo G, Duarte J, Harris P, Hauck S, Liu M, Neubauer MS, Ngadiuba J, Ogrenci-Memik S, Pierini M, Aarrestad T, Bähr S, Becker J, Berthold A-S, Bonventre RJ, Müller Bravo TE, Diefenthaler M, Dong Z, Fritzsche N, Gholami A, Govorkova E, Guo D, Hazelwood KJ, Herwig C, Khan B, Kim S, Klijnsma T, Liu Y, Lo KH, Nguyen T, Pezzullo G, Rasoulinezhad S, Rivera RA, Scholberg K, Selig J, Sen S, Strukov D, Tang W, Thais S, Unger KL, Vilalta R, von Krosigk B, Wang S and Warburton TK (2023) Corrigendum: Applications and techniques for fast machine learning in science. Front. Big Data 6:1301942. doi: 10.3389/fdata.2023.1301942

Received: 25 September 2023; Accepted: 26 September 2023;
Published: 16 October 2023.

Approved by:

Frontiers Editorial Office, Frontiers Media SA, Switzerland

Copyright © 2023 Deiana, Tran, Agar, Blott, Di Guglielmo, Duarte, Harris, Hauck, Liu, Neubauer, Ngadiuba, Ogrenci-Memik, Pierini, Aarrestad, Bähr, Becker, Berthold, Bonventre, Müller Bravo, Diefenthaler, Dong, Fritzsche, Gholami, Govorkova, Guo, Hazelwood, Herwig, Khan, Kim, Klijnsma, Liu, Lo, Nguyen, Pezzullo, Rasoulinezhad, Rivera, Scholberg, Selig, Sen, Strukov, Tang, Thais, Unger, Vilalta, von Krosigk, Wang and Warburton. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Allison McCarn Deiana, YWRlaWFuYSYjeDAwMDQwO3NtdS5lZHU=; Nhan Tran, bnRyYW4mI3gwMDA0MDtmbmFsLmdvdg==

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.