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pro vyhledávání: '"Mckinstry, Jeffrey L"'
Autor:
Bablani, Deepika, Mckinstry, Jeffrey L., Esser, Steven K., Appuswamy, Rathinakumar, Modha, Dharmendra S.
For efficient neural network inference, it is desirable to achieve state-of-the-art accuracy with the simplest networks requiring the least computation, memory, and power. Quantizing networks to lower precision is a powerful technique for simplifying
Externí odkaz:
http://arxiv.org/abs/2301.13330
Autor:
Esser, Steven K., McKinstry, Jeffrey L., Bablani, Deepika, Appuswamy, Rathinakumar, Modha, Dharmendra S.
Deep networks run with low precision operations at inference time offer power and space advantages over high precision alternatives, but need to overcome the challenge of maintaining high accuracy as precision decreases. Here, we present a method for
Externí odkaz:
http://arxiv.org/abs/1902.08153
Autor:
Mckinstry, Jeffrey L, Barch, Davis R., Bablani, Deepika, Debole, Michael V., Esser, Steven K., Kusnitz, Jeffrey A., Arthur, John V., Modha, Dharmendra S.
Low precision networks in the reinforcement learning (RL) setting are relatively unexplored because of the limitations of binary activations for function approximation. Here, in the discrete action ATARI domain, we demonstrate, for the first time, th
Externí odkaz:
http://arxiv.org/abs/1809.09260
Discovering Low-Precision Networks Close to Full-Precision Networks for Efficient Embedded Inference
Autor:
McKinstry, Jeffrey L., Esser, Steven K., Appuswamy, Rathinakumar, Bablani, Deepika, Arthur, John V., Yildiz, Izzet B., Modha, Dharmendra S.
To realize the promise of ubiquitous embedded deep network inference, it is essential to seek limits of energy and area efficiency. To this end, low-precision networks offer tremendous promise because both energy and area scale down quadratically wit
Externí odkaz:
http://arxiv.org/abs/1809.04191
Autor:
Esser, Steven K., Merolla, Paul A., Arthur, John V., Cassidy, Andrew S., Appuswamy, Rathinakumar, Andreopoulos, Alexander, Berg, David J., McKinstry, Jeffrey L., Melano, Timothy, Barch, Davis R., di Nolfo, Carmelo, Datta, Pallab, Amir, Arnon, Taba, Brian, Flickner, Myron D., Modha, Dharmendra S.
Publikováno v:
PNAS 113 (2016) 11441-11446
Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented energy-efficiency through a new chip architecture based on spiking neuron
Externí odkaz:
http://arxiv.org/abs/1603.08270
Akademický článek
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Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 2006 Feb . 103(9), 3387-3392.
Externí odkaz:
https://www.jstor.org/stable/30048592
Publikováno v:
In Neural Networks 2008 21(4):553-561
Charting out the octopus connectome at submicron resolution using the knife-edge scanning microscope
Autor:
Han Donghyeop, Miller Daniel, Mayerich David, Kwon Jaerock, Keyser John, Ponte Giovanna, Abbott Louise C, Choe Yoonsuck, Grimaldi Anna, Fiorito Graziano, Edelman David B, McKinstry Jeffrey L
Publikováno v:
BMC Neuroscience, Vol 11, Iss Suppl 1, p P136 (2010)
Externí odkaz:
https://doaj.org/article/479a7d661f7b44c3b56a17a4faf30041
Autor:
McKinstry, Jeffrey L.1 jlmckins@us.ibm.com, Fleischer, Jason G.1, Chen, Yanqing1, Gall, W. Einar1, Edelman, Gerald M.1
Publikováno v:
PLoS ONE. 21/9/2016, Vol. 11 Issue 9, p1-23. 23p.