Zobrazeno 1 - 10
of 55
pro vyhledávání: '"Myron D. Flickner"'
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
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
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
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/
Autor:
Bryan L. Jackson, Filipp Akopyan, Andrew S. Cassidy, Arnon Amir, Rodrigo Alvarez-Icaza, Rajit Manohar, Steven K. Esser, Paul A. Merolla, John V. Arthur, Myron D. Flickner, Dharmendra S. Modha, Jun Sawada, Yutaka Nakamura, William P. Risk, Ivan Vo, Bernard Brezzo, Rathinakumar Appuswamy, Brian Taba, Nabil Imam, Chen Guo
Publikováno v:
Science. 345:668-673
Modeling computer chips on real brains Computers are nowhere near as versatile as our own brains. Merolla et al. applied our present knowledge of the structure and function of the brain to design a new computer chip that uses the same wiring rules an
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
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
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
Autor:
Rathinakumar Appuswamy, Roger Moussalli, Rajit Manohar, Emmett McQuinn, Andrew S. Cassidy, Christian W. Baks, Paul A. Merolla, Filipp Akopyan, John V. Arthur, Myron D. Flickner, Scott Lekuch, Michael Mastro, Brian Taba, Don Nguyen, Yutaka Nakamura, Sameh W. Asaad, Rodrigo Alvarez-Icaza, C. Haymes, Ken Inoue, Arnon Amir, Marc Gonzalez Tallada, Alexander Andreopoulos, Kai Schleupen, Steve Millman, Daniel Friedman, Steven K. Esser, Jeff Kusnitz, Jun Sawada, Ivan Vo, Bryan L. Jackson, Nabil Imam, Chen Guok, Charles Edwin Cox, Pallab Datta, Bernard Brezzo, Ralph Bellofatto, Dharmendra S. Modha, William P. Risk
Publikováno v:
SC
Drawing on neuroscience, we have developed a parallel, event-driven kernel for neurosynaptic computation, that is efficient with respect to computation, memory, and communication. Building on the previously demonstrated highly optimized software expr
Autor:
Paul A, Merolla, John V, Arthur, Rodrigo, Alvarez-Icaza, Andrew S, Cassidy, Jun, Sawada, Filipp, Akopyan, Bryan L, Jackson, Nabil, Imam, Chen, Guo, Yutaka, Nakamura, Bernard, Brezzo, Ivan, Vo, Steven K, Esser, Rathinakumar, Appuswamy, Brian, Taba, Arnon, Amir, Myron D, Flickner, William P, Risk, Rajit, Manohar, Dharmendra S, Modha
Publikováno v:
Science (New York, N.Y.). 345(6197)
Inspired by the brain's structure, we have developed an efficient, scalable, and flexible non-von Neumann architecture that leverages contemporary silicon technology. To demonstrate, we built a 5.4-billion-transistor chip with 4096 neurosynaptic core