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Autor:
Friedel van Megen, Oren Firestein, Bita Darvish Rouhani, Mahdi Ghandi, Christian Boehn, Prerak Patel, Kara Kagi, Hari Angepat, Doug Burger, Brandon Perez, Raja Seera, Tamas Juhasz, Jeremy Fowers, Shlomi Alkalay, Logan Adams, Gabriel Weisz, Balaji Sridharan, Sangeetha Shekar, Kyle Holohan, Ritchie Zhao, Amanda Rapsang, Ahmad M. El Husseini, Adam Sapek, Todd Massengill, Kalin Ovtcharov, Sitaram Lanka, Dan Zhang, Michael K. Papamichael, Derek Chiou, Lo Daniel, Michael Haselman, Lisa Woods, Kang Su Gatlin, Maleen Abeydeera, Phillip Yi Xiao, Steven K. Reinhardt, Adrian M. Caulfield, Eric S. Chung, Alessandro Forin, Stephen F. Heil, Ratna Kumar Kovvuri, Dima Mukhortov, Ming Liu
Publikováno v:
IEEE Micro. 38:8-20
To meet the computational demands required of deep learning, cloud operators are turning toward specialized hardware for improved efficiency and performance. Project Brainwave, Microsofts principal infrastructure for AI serving in real time, accelera