Zobrazeno 1 - 10
of 62
pro vyhledávání: '"Merolla, Paul"'
Autor:
Appuswamy, Rathinakumar, Nayak, Tapan, Arthur, John, Esser, Steven, Merolla, Paul, Mckinstry, Jeffrey, Melano, Timothy, Flickner, Myron, Modha, Dharmendra
We derive a relationship between network representation in energy-efficient neuromorphic architectures and block Toplitz convolutional matrices. Inspired by this connection, we develop deep convolutional networks using a family of structured convolut
Externí odkaz:
http://arxiv.org/abs/1606.02407
Recent results show that deep neural networks achieve excellent performance even when, during training, weights are quantized and projected to a binary representation. Here, we show that this is just the tip of the iceberg: these same networks, durin
Externí odkaz:
http://arxiv.org/abs/1606.01981
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
Autor:
Diehl, Peter U., Pedroni, Bruno U., Cassidy, Andrew, Merolla, Paul, Neftci, Emre, Zarrella, Guido
We present an approach to constructing a neuromorphic device that responds to language input by producing neuron spikes in proportion to the strength of the appropriate positive or negative emotional response. Specifically, we perform a fine-grained
Externí odkaz:
http://arxiv.org/abs/1601.04183
Autor:
Pedroni, Bruno U., Das, Srinjoy, Arthur, John V., Merolla, Paul A., Jackson, Bryan L., Modha, Dharmendra S., Kreutz-Delgado, Kenneth, Cauwenberghs, Gert
Stochastic neural networks such as Restricted Boltzmann Machines (RBMs) have been successfully used in applications ranging from speech recognition to image classification. Inference and learning in these algorithms use a Markov Chain Monte Carlo pro
Externí odkaz:
http://arxiv.org/abs/1509.07302
Autor:
Das, Srinjoy, Pedroni, Bruno Umbria, Merolla, Paul, Arthur, John, Cassidy, Andrew S., Jackson, Bryan L., Modha, Dharmendra, Cauwenberghs, Gert, Kreutz-Delgado, Ken
Restricted Boltzmann Machines and Deep Belief Networks have been successfully used in a wide variety of applications including image classification and speech recognition. Inference and learning in these algorithms uses a Markov Chain Monte Carlo pro
Externí odkaz:
http://arxiv.org/abs/1503.07793
Artificial neural networks built from two-state neurons are powerful computational substrates, whose computational ability is well understood by analogy with statistical mechanics. In this work, we introduce similar analogies in the context of spikin
Externí odkaz:
http://arxiv.org/abs/1009.5473
Autor:
Merolla, Paul A., Arthur, John V., Alvarez-Icaza, Rodrigo, Cassidy, Andrew S., Sawada, Jun, Akopyan, Filipp, Jackson, Bryan L., Imam, Nabil, Guo, Chen, Nakamura, Yutaka, Brezzo, Bernard, Vo, Ivan, Esser, Steven K., Appuswamy, Rathinakumar, Taba, Brian, Amir, Arnon, Flickner, Myron D., Risk, William P., Manohar, Rajit, Modha, Dharmendra S.
Publikováno v:
Science, 2014 Aug 01. 345(6197), 668-673.
Externí odkaz:
http://www.jstor.org/stable/24745271
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