no code implementations • 8 Jun 2016 • Rathinakumar Appuswamy, Tapan Nayak, John Arthur, Steven Esser, Paul Merolla, Jeffrey Mckinstry, Timothy Melano, Myron Flickner, Dharmendra Modha
We derive a relationship between network representation in energy-efficient neuromorphic architectures and block Toplitz convolutional matrices.
no code implementations • 7 Jun 2016 • Paul Merolla, Rathinakumar Appuswamy, John Arthur, Steve K. Esser, Dharmendra Modha
Recent results show that deep neural networks achieve excellent performance even when, during training, weights are quantized and projected to a binary representation.
no code implementations • 16 Jan 2016 • Peter U. Diehl, Bruno U. Pedroni, Andrew Cassidy, Paul Merolla, Emre Neftci, Guido Zarrella
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.
no code implementations • NeurIPS 2015 • Steve K. Esser, Rathinakumar Appuswamy, Paul Merolla, John V. Arthur, Dharmendra S. Modha
Solving real world problems with embedded neural networks requires both training algorithms that achieve high performance and compatible hardware that runs in real time while remaining energy efficient.
no code implementations • 26 Mar 2015 • Srinjoy Das, Bruno Umbria Pedroni, Paul Merolla, John Arthur, Andrew S. Cassidy, Bryan L. Jackson, Dharmendra Modha, Gert Cauwenberghs, Ken Kreutz-Delgado
Restricted Boltzmann Machines and Deep Belief Networks have been successfully used in a wide variety of applications including image classification and speech recognition.