Search Results - "ARTHUR, John V"

Convolutional networks for fast, energy-efficient neuromorphic computing by 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 Nolfoa, Carmelo, Datta, Pallab, Amir, Arnon, Taba, Brian, Flickner, Myron D., Modha, Dharmendra S.

“…Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now…”
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TrueNorth: Accelerating From Zero to 64 Million Neurons in 10 Years by DeBole, Michael V., Taba, Brian, Amir, Arnon, Akopyan, Filipp, Andreopoulos, Alexander, Risk, William P., Kusnitz, Jeff, Ortega Otero, Carlos, Nayak, Tapan K., Appuswamy, Rathinakumar, Carlson, Peter J., Cassidy, Andrew S., Datta, Pallab, Esser, Steven K., Garreau, Guillaume J., Holland, Kevin L., Lekuch, Scott, Mastro, Michael, McKinstry, Jeff, di Nolfo, Carmelo, Paulovicks, Brent, Sawada, Jun, Schleupen, Kai, Shaw, Benjamin G., Klamo, Jennifer L., Flickner, Myron D., Arthur, John V., Modha, Dharmendra S.

“…IBM's brain-inspired processor is a massively parallel neural network inference engine containing 1 million spiking neurons and 256 million low-precision…”
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Expandable Networks for Neuromorphic Chips by Merolla, P.A., Arthur, J.V., Shi, B.E., Boahen, K.A.

“…We have developed a grid network that broadcasts spikes (all-or-none events) in a multichip neuromorphic system by relaying them from chip to chip. The grid is…”
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Always-On Speech Recognition Using TrueNorth, a Reconfigurable, Neurosynaptic Processor by Wei-Yu Tsai, Barch, Davis R., Cassidy, Andrew S., DeBole, Michael V., Andreopoulos, Alexander, Jackson, Bryan L., Flickner, Myron D., Arthur, John V., Modha, Dharmendra S., Sampson, John, Narayanan, Vijaykrishnan

“…Deep neural networks (DNN) have been shown to be very effective at solving challenging problems in several areas of computing, including vision, speech, and…”
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A million spiking-neuron integrated circuit with a scalable communication network and interface by 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.

“…Inspired by the brain's structure, we have developed an efficient, scalable, and flexible non–von Neumann architecture that leverages contemporary silicon…”
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Implementation of olfactory bulb glomerular-layer computations in a digital neurosynaptic core by Imam, Nabil, Cleland, Thomas A, Manohar, Rajit, Merolla, Paul A, Arthur, John V, Akopyan, Filipp, Modha, Dharmendra S

“…We present a biomimetic system that captures essential functional properties of the glomerular layer of the mammalian olfactory bulb, specifically including…”
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Silicon-Neuron Design: A Dynamical Systems Approach by Arthur, J V, Boahen, K A

“…We present an approach to design spiking silicon neurons based on dynamical systems theory. Dynamical systems theory aids in choosing the appropriate level of…”
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Neural inference at the frontier of energy, space, and time by Modha, Dharmendra S., Akopyan, Filipp, Andreopoulos, Alexander, Appuswamy, Rathinakumar, Arthur, John V., Cassidy, Andrew S., Datta, Pallab, DeBole, Michael V., Esser, Steven K., Otero, Carlos Ortega, Sawada, Jun, Taba, Brian, Amir, Arnon, Bablani, Deepika, Carlson, Peter J., Flickner, Myron D., Gandhasri, Rajamohan, Garreau, Guillaume J., Ito, Megumi, Klamo, Jennifer L., Kusnitz, Jeffrey A., McClatchey, Nathaniel J., McKinstry, Jeffrey L., Nakamura, Yutaka, Nayak, Tapan K., Risk, William P., Schleupen, Kai, Shaw, Ben, Sivagnaname, Jay, Smith, Daniel F., Terrizzano, Ignacio, Ueda, Takanori

“…Computing, since its inception, has been processor-centric, with memory separated from compute. Inspired by the organic brain and optimized for inorganic…”
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Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations by Benjamin, Ben Varkey, Boahen, Kwabena, Gao, Peiran, McQuinn, Emmett, Choudhary, Swadesh, Chandrasekaran, Anand R., Bussat, Jean-Marie, Alvarez-Icaza, Rodrigo, Arthur, John V., Merolla, Paul A.

“…In this paper, we describe the design of Neurogrid, a neuromorphic system for simulating large-scale neural models in real time. Neuromorphic systems realize…”
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Mapping Generative Models onto a Network of Digital Spiking Neurons by 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…”
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Synchrony in Silicon: The Gamma Rhythm by Arthur, J.V., Boahen, K.A.

“…In this paper, we present a network of silicon in-terneurons that synchronize in the gamma frequency range (20-80 Hz). The gamma rhythm strongly influences…”
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Discovering Low-Precision Networks Close to Full-Precision Networks for Efficient Inference by McKinstry, Jeffrey L., Esser, Steven K., Appuswamy, Rathinakumar, Bablani, Deepika, Arthur, John V., Yildiz, Izzet B., Modha, Dharmendra S.

“…To realize the promise of ubiquitous embedded deep network inference, it is essential to seek limits of energy and area efficiency. Low-precision networks…”
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11.4 IBM NorthPole: An Architecture for Neural Network Inference with a 12nm Chip by Cassidy, Andrew S., Arthur, John V., Akopyan, Filipp, Andreopoulos, Alexander, Appuswamy, Rathinakumar, Datta, Pallab, Debole, Michael V., Esser, Steven K., Otero, Carlos Ortega, Sawada, Jun, Taba, Brian, Amir, Arnon, Bablani, Deepika, Carlson, Peter J., Flickner, Myron D., Gandhasri, Rajamohan, Garreau, Guillaume J., Ito, Megumi, Klamo, Jennifer L., Kusnitz, Jeffrey A., McClatchey, Nathaniel J., McKinstry, Jeffrey L., Nakamura, Yutaka, Nayak, Tapan K., Risk, William P., Schleupen, Kai, Shaw, Ben, Sivagnaname, Jay, Smith, Daniel F., Terrizzano, Ignacio, Ueda, Takanori, Modha, Dharmendra

“…The Deep Neural Network (DNN) era was ushered in by the triad of algorithms, big data, and more powerful hardware processors for training large-scale neural…”
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Cognitive computing building block: A versatile and efficient digital neuron model for neurosynaptic cores by Cassidy, Andrew S., Merolla, Paul, Arthur, John V., Esser, Steve K., Jackson, Bryan, Alvarez-Icaza, Rodrigo, Datta, Pallab, Sawada, Jun, Wong, Theodore M., Feldman, Vitaly, Amir, Arnon, Rubin, Daniel Ben-Dayan, Akopyan, Filipp, McQuinn, Emmett, Risk, William P., Modha, Dharmendra S.

“…Marching along the DARPA SyNAPSE roadmap, IBM unveils a trilogy of innovations towards the TrueNorth cognitive computing system inspired by the brain's…”
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IBM NorthPole Neural Inference Machine by Modha, Dharmendra S., Akopyan, Filipp, Andreopoulos, Alexander, Appuswamy, Rathinakumar, Arthur, John V., Cassidy, Andrew S., Datta, Pallab, DeBole, Michael V., Esser, Steven K., Otero, Carlos Ortega, Sawada, Jun, Taba, Brian, Amir, Arnon, Bablani, Deepika, Carlson, Peter J., Flickner, Myron D., Gandhasri, Rajamohan, Garreau, Guillaume J., Ito, Megumi, Klamo, Jennifer L., Kusnitz, Jeffrey A., McClatchey, Nathaniel J., McKinstry, Jeffrey L., Nakamura, Yutaka, Nayak, Tapan K., Risk, William P., Schleupen, Kai, Shaw, Ben, Sivagnaname, Jay, Smith, Daniel F., Terrizzano, Ignacio, Ueda, Takanori

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Building block of a programmable neuromorphic substrate: A digital neurosynaptic core by Arthur, J. V., Merolla, P. A., Akopyan, F., Alvarez, R., Cassidy, A., Chandra, S., Esser, S. K., Imam, N., Risk, W., Rubin, D. B. D., Manohar, R., Modha, D. S.

“…The grand challenge of neuromorphic computation is to develop a flexible brain-inspired architecture capable of a wide array of real-time applications, while…”
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A superposable silicon synapse with programmable reversal potential by Benjamin, B. V., Arthur, J. V., Peiran Gao, Merolla, P., Boahen, K.

“…We present a novel log-domain silicon synapse designed for subthreshold analog operation that emulates common synaptic interactions found in biology. Our…”
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Recurrently connected silicon neurons with active dendrites for one-shot learning by Arthur, J.V., Boahen, K.

“…We describe a neuromorphic chip designed to model active dendrites, recurrent connectivity, and plastic synapses to support one-shot learning. Specifically, it…”
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Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations: Engineering Intelligent Electronic Systems Based on Computational Neuroscience by BENJAMIN, Ben Varkey, GAO, Peiran, MCQUINN, Emmett, CHOUDHARY, Swadesh, CHANDRASEKARAN, Anand R, BUSSAT, Jean-Marie, ALVAREZ-ICAZA, Rodrigo, ARTHUR, John V, MEROLLA, Paul A, BOAHEN, Kwabena

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Neuromorphic implementation of orientation hypercolumns by Choi, T.Y.W., Merolla, P.A., Arthur, J.V., Boahen, K.A., Shi, B.E.

“…Neurons in the mammalian primary visual cortex are selective along multiple stimulus dimensions, including retinal position, spatial frequency, and…”
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