Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Ryan Dellana"'
Autor:
Megan M. Baker, Alexander New, Mario Aguilar-Simon, Ziad Al-Halah, Sébastien M.R. Arnold, Ese Ben-Iwhiwhu, Andrew P. Brna, Ethan Brooks, Ryan C. Brown, Zachary Daniels, Anurag Daram, Fabien Delattre, Ryan Dellana, Eric Eaton, Haotian Fu, Kristen Grauman, Jesse Hostetler, Shariq Iqbal, Cassandra Kent, Nicholas Ketz, Soheil Kolouri, George Konidaris, Dhireesha Kudithipudi, Erik Learned-Miller, Seungwon Lee, Michael L. Littman, Sandeep Madireddy, Jorge A. Mendez, Eric Q. Nguyen, Christine Piatko, Praveen K. Pilly, Aswin Raghavan, Abrar Rahman, Santhosh Kumar Ramakrishnan, Neale Ratzlaff, Andrea Soltoggio, Peter Stone, Indranil Sur, Zhipeng Tang, Saket Tiwari, Kyle Vedder, Felix Wang, Zifan Xu, Angel Yanguas-Gil, Harel Yedidsion, Shangqun Yu, Gautam K. Vallabha
Publikováno v:
Neural Networks. 160:274-296
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::036018c7a4ebaf09e61ba1d46f5b4c3c
https://doi.org/10.1016/j.neunet.2023.01.007
https://doi.org/10.1016/j.neunet.2023.01.007
Autor:
Craig Vineyard, Andrew Sornborger, James Aimone, Abrar Anwar, Ryan Dellana, Esteban Guillen, Mark Plagge, William Severa, Javier Zazueta, Diego Arana, Oleksandr Iaroshenko, Gerd Kunde, Alpha Renner, Anatoly Zlotnik
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8078fa1855071079a1adca6be9d8d1ca
https://doi.org/10.2172/1891944
https://doi.org/10.2172/1891944
Publikováno v:
Nature Machine Intelligence. 1:86-94
The computational cost of deep neural networks presents challenges to broadly deploying these algorithms. Low-power and embedded neuromorphic processors offer potentially dramatic performance-per-watt improvements over traditional processors. However
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d4a03ec8d91da5b35b6872d370292709
https://doi.org/10.1038/s42256-018-0015-y
https://doi.org/10.1038/s42256-018-0015-y
Autor:
Christopher Bennett, Ryan Dellana, Tianyao Xiao, Benjamin Feinberg, Sapan Agarwal, Suma Cardwell, Matthew Marinella, William Severa, James Aimone
Publikováno v:
Proposed for presentation at the NICE Virtual Conference Proceedings in n/a, n/a..
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7b5e426aec233f03bb9a19159ba2e33f
https://doi.org/10.2172/1859699
https://doi.org/10.2172/1859699
Publikováno v:
ICONS
Deep learning networks have become a vital tool for image and data processing tasks for deployed and edge applications. Resource constraints, particularly low power budgets, have motivated methods and devices for efficient on-edge inference. Two prom
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7e865e880d7388065e7643fa7384c7d2
https://doi.org/10.1145/3407197.3407201
https://doi.org/10.1145/3407197.3407201
Autor:
Ben Feinberg, Vijay Raghavan, Krishnaswamy Ramkumar, Vineet Agrawal, Ryan Dellana, Matthew J. Marinella, Swatilekha Saha, Venkatraman Prabhakar, Ramesh Chettuvetty, Christopher H. Bennett, Sapan Agarwal, Long Hinh, T. Patrick Xiao
Publikováno v:
IRPS
Non-volatile memory arrays can deploy pre-trained neural network models for edge inference. However, these systems are affected by device-level noise and retention issues. Here, we examine damage caused by these effects, introduce a mitigation strate
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3dcb9fed2cc75db7ffabfc0bab1a0556
http://arxiv.org/abs/2004.00802
http://arxiv.org/abs/2004.00802
Autor:
Brad Aimone, Matthew J. Marinella, Suma Cardwell, Ben Feinberg, Ryan Dellana, Sapan Agarwal, Christopher H. Bennett, William Severa, T. Patrick Xiao
Publikováno v:
NICE
Neuromorphic-style inference only works well if limited hardware resources are maximized properly, e.g. accuracy continues to scale with parameters and complexity in the face of potential disturbance. In this work, we use realistic crossbar simulatio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bea5d416a36facde142f6ea0108ecb67