Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Katie Spoon"'
Autor:
Katie Spoon, Hsinyu Tsai, An Chen, Malte J. Rasch, Stefano Ambrogio, Charles Mackin, Andrea Fasoli, Alexander M. Friz, Pritish Narayanan, Milos Stanisavljevic, Geoffrey W. Burr
Publikováno v:
Frontiers in Computational Neuroscience, Vol 15 (2021)
Recent advances in deep learning have been driven by ever-increasing model sizes, with networks growing to millions or even billions of parameters. Such enormous models call for fast and energy-efficient hardware accelerators. We study the potential
Externí odkaz:
https://doaj.org/article/0ea5e2126e9644a1892bfada90cc4cb8
Autor:
Katie Spoon, Nicholas LaBerge, K. Hunter Wapman, Sam Zhang, Allison Morgan, Mirta Galesic, Bailey Fosdick, Daniel Larremore, Aaron Clauset
Women remain underrepresented as faculty in nearly all academic fields, a pattern often attributed in part to gendered retention rates, in which women leave faculty jobs at higher rates over a career than men. However, the magnitude and variation of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::84e6e587192507976c98d17feb2b0022
https://doi.org/10.31235/osf.io/u26ze
https://doi.org/10.31235/osf.io/u26ze
Autor:
I. Ok, Samuel S. Choi, Riduan Khaddam-Aljameh, Scott C. Lewis, Charles Mackin, Wilfried Haensch, Kevin W. Brew, Victor Chan, F. Lie, Alexander Friz, Stefano Ambrogio, Marc A. Bergendahl, James J. Demarest, Geoffrey W. Burr, Akiyo Nomura, Atsuya Okazaki, Katie Spoon, Takeo Yasuda, Masatoshi Ishii, Nicole Saulnier, Ishtiaq Ahsan, Pritish Narayanan, Hsinyu Tsai, Vijay Narayanan, Hiroyuki Mori, Y. Kohda, Kohji Hosokawa
Publikováno v:
IEEE Transactions on Electron Devices. 68:6629-6636
Hardware acceleration of deep learning using analog non-volatile memory (NVM) requires large arrays with high device yield, high accuracy Multiply-ACcumulate (MAC) operations, and routing frameworks for implementing arbitrary deep neural network (DNN
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a8d47a1ee53bb906bdde9b10caecdc5a
https://doi.org/10.1109/ted.2021.3115993
https://doi.org/10.1109/ted.2021.3115993
Autor:
Hsinyu Tsai, An Chen, Charles Mackin, Pritish Narayanan, Geoffrey W. Burr, Stefano Ambrogio, Sanjay Kariyappa, Katie Spoon, Moinuddin K. Qureshi
Publikováno v:
IEEE Transactions on Electron Devices. 68:4356-4362
Phase change memory (PCM)-based “Analog-AI” accelerators are gaining importance for inference in edge applications due to the energy efficiency offered by in-memory computing. Nevertheless, noise sources inherent to PCM devices cause inaccuracies
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b9a7a6a9ff14073db5b5b3831f68180d
https://doi.org/10.1109/ted.2021.3089987
https://doi.org/10.1109/ted.2021.3089987
Publikováno v:
European Burn Journal, Vol 5, Iss 3, Pp 198-206 (2024)
Scars following burns can often prove complex to manage, particularly when crossing joints or special areas such as the head and neck, due to contractures. This case report discusses the individualised care and rehabilitation provided to a burn patie
Externí odkaz:
https://doaj.org/article/d2e309be3d3f492eae124733de2d11a2
Autor:
Atsuya Okazaki, Pritish Narayanan, Stefano Ambrogio, Kohji Hosokawa, Hsinyu Tsai, Akiyo Nomura, Takeo Yasuda, Charles Mackin, Alexander Friz, Masatoshi Ishii, Yasuteru Kohda, Katie Spoon, An Chen, Andrea Fasoli, Malte J. Rasch, Geoffrey W. Burr
Publikováno v:
2022 IEEE International Symposium on Circuits and Systems (ISCAS).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::78fccef90fd2719a41c4b0f978cbae68
https://doi.org/10.1109/iscas48785.2022.9937292
https://doi.org/10.1109/iscas48785.2022.9937292
Autor:
Katie Spoon, Stefano Ambrogio, Pritish Narayanan, Hsinyu Tsai, Charles Mackin, An Chen, Andrea Fasoli, Alexander Friz, Geoffrey W. Burr
Publikováno v:
Machine Learning and Non-volatile Memories ISBN: 9783031038402
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3aaf24a72829ef921281d43e78913aba
https://doi.org/10.1007/978-3-031-03841-9_3
https://doi.org/10.1007/978-3-031-03841-9_3
Autor:
Pritish Narayanan, Katie Spoon, Charles Mackin, Geoffrey W. Burr, Andrea Fasoli, Stefano Ambrogio, An Chen, Hsinyu Tsai, Malte J. Rasch, Alexander Friz, Milos Stanisavljevic
Publikováno v:
Frontiers in Computational Neuroscience, Vol 15 (2021)
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience
Recent advances in deep learning have been driven by ever-increasing model sizes, with networks growing to millions or even billions of parameters. Such enormous models call for fast and energy-efficient hardware accelerators. We study the potential
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d8dcf09aefdb4f8e747581014c650091
https://www.frontiersin.org/articles/10.3389/fncom.2021.675741/full
https://www.frontiersin.org/articles/10.3389/fncom.2021.675741/full
Autor:
Robert L. Bruce, Jin-Ping Han, I. Ok, Abu Sebastian, Geoffrey W. Burr, John M. Papalia, Hsinyu Tsai, Vijay Narayanan, Lynne Gignac, Katie Spoon, Tenko Yamashita, Nicole Saulnier, S. R. Nandakumar, Cheng-Wei Cheng, Andrew H. Simon, Benedikt Kersting, Charles Mackin, Irem Boybat, Stefano Ambrogio, Kevin W. Brew, Matthew J. BrightSky, Ning Li, M. Le Gallo, Praneet Adusumilli, Saraf Iqbal Rashid, Timothy M. Philip, Wanki Kim, Zuoguang Liu, Thomas Bohnstingl, S. Ghazi Sarwat, Nanbo Gong
Publikováno v:
IRPS
Phase change memory (PCM) is rapidly emerging as a promising candidate for building non-von Neumann accelerators for deep neural networks (DNN) based on in-memory computing. However, conductance drift and noise are key challenges for the reliable sto
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::89a3aa0be0be14099a1292533593cbc5
https://doi.org/10.1109/irps46558.2021.9405191
https://doi.org/10.1109/irps46558.2021.9405191
Autor:
Katie Spoon, Stefano Ambrogio, Pritish Narayanan, Hsinyu Tsai, Charles Mackin, An Chen, Alexander Friz, Geoffrey W. Burr, Andrea Fasoli
Publikováno v:
AICAS
Analog memory offers enormous potential to speed up computation in deep learning. We study the use of phase-change memory (PCM) as the resistive element in a crossbar array that allows the multiply-accumulate operation in deep neural networks to be p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::13de99f61044e6c8c35a6934f3030ce8
https://doi.org/10.1109/aicas48895.2020.9073978
https://doi.org/10.1109/aicas48895.2020.9073978