Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Alexander Andreopoulos"'
Autor:
Peter J. Carlson, Michael DeBole, John V. Arthur, Myron D. Flickner, Scott Lekuch, Jeffrey L. McKinstry, Andrew S. Cassidy, Michael Mastro, Jeff Kusnitz, Brian Taba, Carmelo di Nolfo, Rathinakumar Appuswamy, Jun Sawada, Steven K. Esser, Pallab Datta, Brent Paulovicks, Klamo Jennifer, Kai Schleupen, Kevin L. Holland, Arnon Amir, Guillaume J. Garreau, Filipp Akopyan, Dharmendra S. Modha, Benjamin G. Shaw, Alexander Andreopoulos, Tapan K. Nayak, Carlos Tadeo Ortega Otero, William P. Risk
Publikováno v:
Computer. 52:20-29
IBM's brain-inspired processor is a massively parallel neural network inference engine containing 1 million spiking neurons and 256 million low-precision synapses. Now, after a decade of fundamental research spanning neuroscience, architecture, chips
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::49b898399142d35f409e1c3c3bdb2c8b
https://doi.org/10.1109/mc.2019.2903009
https://doi.org/10.1109/mc.2019.2903009
Autor:
Bryan L. Jackson, Andrew S. Cassidy, John V. Arthur, Myron D. Flickner, Jack Sampson, R Davis, Dharmendra S. Modha, Alexander Andreopoulos, Wei-Yu Tsai, Michael DeBole, Vijaykrishnan Narayanan
Publikováno v:
IEEE Transactions on Computers. 66:996-1007
Deep neural networks (DNN) have been shown to be very effective at solving challenging problems in several areas of computing, including vision, speech, and natural language processing. However, traditional platforms for implementing these DNNs are o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c355d079b162f4bb28e2d07c68a17cd9
https://doi.org/10.1109/tc.2016.2630683
https://doi.org/10.1109/tc.2016.2630683
Publikováno v:
ICCV Workshops
This paper proposes that despite the success of deep learning methods in computer vision, the dominance we see would not have been possible by the methods of deep learning alone: the tacit change has been the evolution of empirical practice in comput
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::21d318124963f06b3d810fee66702512
https://doi.org/10.1109/iccvw.2019.00260
https://doi.org/10.1109/iccvw.2019.00260
Publikováno v:
CVPR
We introduce a stereo correspondence system implemented fully on event-based digital hardware, using a fully graph-based non von-Neumann computation model, where no frames, arrays, or any other such data-structures are used. This is the first time th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cb9fc955df838b7a7327b1585f301ac4
https://doi.org/10.1109/cvpr.2018.00786
https://doi.org/10.1109/cvpr.2018.00786
Autor:
Michael DeBole, Alexander Andreopoulos, David Berg, Brian Taba, Arnon Amir, Jeffrey L. McKinstry, Marcela Mendoza, Timothy Melano, Dharmendra S. Modha, Tapan K. Nayak, Jeff Kusnitz, Steve K. Esser, Guillaume Garreau, Tobi Delbruck, Myron D. Flickner, Carmelo di Nolfo
Publikováno v:
CVPR
We present the first gesture recognition system implemented end-to-end on event-based hardware, using a TrueNorth neurosynaptic processor to recognize hand gestures in real-time at low power from events streamed live by a Dynamic Vision Sensor (DVS).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e375371de0f2cc1090ac170933a7a400
https://www.zora.uzh.ch/id/eprint/149342/
https://www.zora.uzh.ch/id/eprint/149342/
Publikováno v:
Computer Vision and Image Understanding. 117:827-891
Object recognition systems constitute a deeply entrenched and omnipresent component of modern intelligent systems. Research on object recognition algorithms has led to advances in factory and office automation through the creation of optical characte
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::46acc4149cce857ff286d73d527c315e
https://doi.org/10.1016/j.cviu.2013.04.005
https://doi.org/10.1016/j.cviu.2013.04.005
Autor:
Jun Sawada, Filipp Akopyan, Andrew S. Cassidy, Brian Taba, Michael V. Debole, Pallab Datta, Rodrigo Alvarez-Icaza, Arnon Amir, John V. Arthur, Alexander Andreopoulos, Rathinakumar Appuswamy, Heinz Baier, Davis Barch, David J. Berg, Carmelo Di Nolfo, Steven K. Esser, Myron Flickner, Thomas A. Horvath, Bryan L. Jackson, Jeff Kusnitz, Scott Lekuch, Michael Mastro, Timothy Melano, Paul A. Merolla, Steven E. Millman, Tapan K. Nayak, Norm Pass, Hartmut E. Penner, William P. Risk, Kai Schleupen, Benjamin Shaw, Hayley Wu, Brian Giera, Adam T. Moody, Nathan Mundhenk, Brian C. Van Essen, Eric X. Wang, David P. Widemann, Qing Wu, William E. Murphy, Jamie K. Infantolino, James A. Ross, Dale R. Shires, Manuel M. Vindiola, Raju Namburu, Dharmendra S. Modha
Publikováno v:
SC16: International Conference for High Performance Computing, Networking, Storage and Analysis.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4eb353a85553254286eef8f2d979b3c7
https://doi.org/10.1109/sc.2016.11
https://doi.org/10.1109/sc.2016.11
Autor:
Bryan L. Jackson, Andrew S. Cassidy, Wei-Yu Tsai, Michael DeBole, Dharmendra S. Modha, Alexander Andreopoulos, Myron D. Flickner, Jack Sampson, Vijaykrishnan Narayanan, R Davis
Publikováno v:
IJCNN
With recent advances in silicon technology, previously intractable Deep Neural Network (DNN) solutions to complex visual, auditory, and other sensory perception problems are now practical for real-time, energy constrained systems. One such advancemen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fa0750b0ddd0f7c496450dae05905056
https://doi.org/10.1109/ijcnn.2016.7727757
https://doi.org/10.1109/ijcnn.2016.7727757
Publikováno v:
IJCNN
We demonstrate how to map Local Binary Patterns (LBP), a class of leading feature extractors, onto a neuromorphic processor such as TrueNorth, a silicon expression of a non-von Neumann, low-power, spiking-based, brain-inspired processor. The applicat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8b649d0d01eb370e028c7805209e1bbb
https://doi.org/10.1109/ijcnn.2016.7727762
https://doi.org/10.1109/ijcnn.2016.7727762
Autor:
Andrew S. Cassidy, Carmelo di Nolfo, David Berg, Brian Taba, Paul A. Merolla, Pallab Datta, John V. Arthur, Myron D. Flickner, Jeffrey L. McKinstry, Rathinakumar Appuswamy, Arnon Amir, Steven K. Esser, Alexander Andreopoulos, R Davis, Dharmendra S. Modha, Timothy Melano
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9053823adca098d05381183eb15f2b06
http://arxiv.org/abs/1603.08270
http://arxiv.org/abs/1603.08270