Zobrazeno 1 - 10
of 307
pro vyhledávání: '"R. Scheuermann"'
Publikováno v:
International Journal of Infectious Diseases, Vol 101, Iss , Pp 231- (2020)
Externí odkaz:
https://doaj.org/article/aa7025b334774ca894124fff9bc68d71
Publikováno v:
International Journal of Infectious Diseases, Vol 101, Iss , Pp 242- (2020)
Externí odkaz:
https://doaj.org/article/117c176626ac4181a9b32a09f1b541b9
Autor:
M. H. Dehn, R. Scheuermann, P.-X. Wang, Y. Cao, M. J. MacLachlan, V. M. Zamarion, D. G. Fleming, R. F. Kiefl
Publikováno v:
Physical Review Research, Vol 3, Iss 1, p 013029 (2021)
Precise measurements of the muonium (Mu) hyperfine interaction versus temperature are reported in a silica aerogel and mesoporous silica SBA-15 using a fast-timing spectrometer to detect the precession frequencies of Mu in a magnetic field of 1.14 T.
Externí odkaz:
https://doaj.org/article/e281f0f62a674fe29053fed33e3727b7
Akademický článek
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Autor:
Jinwook Oh, Alyssa Herbert, Marcel Schaal, Zhibin Ren, Ching Zhou, Siyu Koswatta, Naigang Wang, Matthew Cohen, Vidhi Zalani, Howard M. Haynie, Matthew M. Ziegler, Sae Kyu Lee, Brian W. Curran, Monodeep Kar, Martin Lutz, Xin Zhang, Robert Casatuta, Vijayalakshmi Srinivasan, Nianzheng Cao, Sunil Shukla, Pong-Fei Lu, Leland Chang, Michael A. Guillorn, Bruce M. Fleischer, Michael R. Scheuermann, Joel Abraham Silberman, Kerstin Schelm, Vinay Velji Shah, Chia-Yu Chen, Kailash Gopalakrishnan, Swagath Venkataramani, Hung Tran, Mingu Kang, Wei Wang, Jungwook Choi, Scot H. Rider, Jinwook Jung, James J. Bonanno, Radhika Jain, Li Yulong, Xiao Sun, Silvia Melitta Mueller, Kyu-hyoun Kim, Ankur Agrawal
Publikováno v:
IEEE Journal of Solid-State Circuits. 57:182-197
Reduced precision computation is a key enabling factor for energy-efficient acceleration of deep learning (DL) applications. This article presents a 7-nm four-core mixed-precision artificial intelligence (AI) chip that supports four compute precision
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2998556a11fb9c2dbdc4775ef363b4bb
https://doi.org/10.1109/jssc.2021.3120113
https://doi.org/10.1109/jssc.2021.3120113
Autor:
Michael J. Klaiber, George D. Gristede, Shih-Hsien Lo, Hiroshi Inoue, Leland Chang, Christos Vezyrtzis, Jungwook Choi, Gary W. Maier, Fanchieh Yee, Shubham Jain, Brian W. Curran, Jintao Zhang, Mingu Kang, Howard M. Haynie, Mauricio J. Serrano, Pong-Fei Lu, Silvia Melitta Mueller, Matthew M. Ziegler, Bruce M. Fleischer, Kazuaki Ishizaki, Kailash Gopalakrishnan, Michael R. Scheuermann, Ankur Agarwal, Xiao Sun, Sunil Shukla, Thomas W. Fox, Vijayalakshmi Srinivasan, Tina Babinsky, Swagath Venkataramani, Michael A. Guillorn, Ching Zhou, Nianzheng Cao, Eri Ogawa, Naigang Wang, Moriyoshi Ohara, Joel Abraham Silberman, Jinwook Oh, Marcel Schaal, Chia-Yu Chen, Wei Wang
Publikováno v:
Proceedings of the IEEE. 108:2232-2250
Advances in deep neural networks (DNNs) and the availability of massive real-world data have enabled superhuman levels of accuracy on many AI tasks and ushered the explosive growth of AI workloads across the spectrum of computing devices. However, th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a039fc675f0bb5ab8047808cf3959486
https://doi.org/10.1109/jproc.2020.3029453
https://doi.org/10.1109/jproc.2020.3029453
Akademický článek
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Akademický článek
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Autor:
Ann H. Klopp, Song Gao, James M. Metz, R Scheuermann, Mu Young Lee, Juha Kauppinen, Lei Dong, C. Clifton Ling, Pekka Uusitalo, C. Kennedy, Laurence E. Court, M Constantin, Dimitris Mihailidis, Peter A Balter, Tucker Netherton, Yuting Li, Stephen Thompson
Publikováno v:
Medical Physics. 46:4304-4313
Purpose This manuscript describes the experience of two institutions in commissioning the new HalcyonTM platform. Its purpose is to: (a) validate the pre-defined beam data, (b) compare relevant commissioning data acquired independently by two separat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::80ca59848714f264f665fbe9cf5c2769
https://doi.org/10.1002/mp.13723
https://doi.org/10.1002/mp.13723
Autor:
Scot H. Rider, Martin Lutz, Moriyoshi Ohara, Pong-Fei Lu, Monodeep Kar, Xiao Sun, Kailash Gopalakrishnan, Jie Yang, Hoang Tran, Wei Wang, Michael A. Guillorn, Marcel Schaal, Ankur Agrawal, Xin Zhang, Joel Abraham Silberman, Sunil Shukla, Nianzheng Cao, James Bonano, Zhibin Ren, Sanchari Sen, Siyu Koswatta, Kyu-hyoun Kim, Mingu Kang, Swagath Venkataramani, Eri Ogawa, Vijayalakshmi Srinivasan, Hiroshi Inoue, Matt Ziegler, Howard M. Haynie, Shubham Jain, Vinay Velji Shah, Allison Allain, Jintao Zhang, Matthew Cohen, Jungwook Choi, Kerstin Schelm, Jinwook Oh, Li Yulong, Chia-Yu Chen, Ching Zhou, Naigang Wang, Jinwook Jung, Sae Kyu Lee, Silvia Melitta Mueller, Kazuaki Ishizaki, Bruce M. Fleischer, Michael R. Scheuermann, Vidhi Zalani, Brian W. Curran, Leland Chang, Mauricio J. Serrano, Ashish Ranjan, Alberto Mannari, Robert Casatuta
Publikováno v:
ISCA
The growing prevalence and computational demands of Artificial Intelligence (AI) workloads has led to widespread use of hardware accelerators in their execution. Scaling the performance of AI accelerators across generations is pivotal to their succes
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::edc72fc1439c98a561b7e0104cf8a62a
https://doi.org/10.1109/isca52012.2021.00021
https://doi.org/10.1109/isca52012.2021.00021