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ORCIDArnon Amir
- > Home > Persons > Arnon Amir
Publications
- 2016
[c46]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 G. Shaw, Hayley Wu, Brian Giera, Adam T. Moody, T. Nathan Mundhenk, Brian 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:
Truenorth ecosystem for brain-inspired computing: scalable systems, software, and applications. SC 2016: 130-141- 2014
- [c45]Andrew S. Cassidy, Rodrigo Alvarez-Icaza, Filipp Akopyan, Jun Sawada, John V. Arthur, Paul Merolla, Pallab Datta, Marc González Tallada, Brian Taba, Alexander Andreopoulos, Arnon Amir, Steven K. Esser, Jeff Kusnitz, Rathinakumar Appuswamy, Chuck Haymes, Bernard Brezzo, Roger Moussalli, Ralph Bellofatto, Christian W. Baks, Michael Mastro, Kai Schleupen, Charles E. Cox, Ken Inoue, Steven E. Millman, Nabil Imam, Emmett McQuinn, Yutaka Y. Nakamura, Ivan Vo, Chen Guok, Don Nguyen, Scott Lekuch, Sameh W. Asaad, Daniel J. Friedman, Bryan L. Jackson, Myron Flickner, William P. Risk, Rajit Manohar, Dharmendra S. Modha:
Real-Time Scalable Cortical Computing at 46 Giga-Synaptic OPS/Watt with ~100× Speedup in Time-to-Solution and ~100, 000× Reduction in Energy-to-Solution. SC 2014: 27-38 - 2013
- [c43]Andrew S. Cassidy, Paul Merolla, John V. Arthur, Steven K. Esser, Bryan L. Jackson, Rodrigo Alvarez-Icaza, Pallab Datta, Jun Sawada, Theodore M. Wong, Vitaly Feldman, Arnon Amir, Daniel Ben Dayan Rubin, Filipp Akopyan, Emmett McQuinn, William P. Risk, Dharmendra S. Modha:
Cognitive computing building block: A versatile and efficient digital neuron model for neurosynaptic cores. IJCNN 2013: 1-10
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