J
John V. Arthur
Researcher at IBM
Publications - 107
Citations - 11427
John V. Arthur is an academic researcher from IBM. The author has contributed to research in topics: Neuromorphic engineering & TrueNorth. The author has an hindex of 30, co-authored 107 publications receiving 9258 citations. Previous affiliations of John V. Arthur include University of Pennsylvania & Stanford University.
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A million spiking-neuron integrated circuit with a scalable communication network and interface
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 +19 more
TL;DR: Inspired by the brain’s structure, an efficient, scalable, and flexible non–von Neumann architecture is developed that leverages contemporary silicon technology and is well suited to many applications that use complex neural networks in real time, for example, multiobject detection and classification.
Journal ArticleDOI
Neuromorphic Silicon Neuron Circuits
Giacomo Indiveri,Bernabe Linares-Barranco,Tara Julia Hamilton,André van Schaik,Ralph Etienne-Cummings,Tobi Delbruck,Shih-Chii Liu,Piotr Dudek,Philipp Hafliger,Sylvie Renaud,Johannes Schemmel,Gert Cauwenberghs,John V. Arthur,Kai Hynna,Fopefolu Folowosele,Sylvain Saïghi,Teresa Serrano-Gotarredona,Jayawan H B Wijekoon,Yingxue Wang,Kwabena Boahen +19 more
TL;DR: The most common building blocks and techniques used to implement these circuits, and an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance-based Hodgkin–Huxley models to bi-dimensional generalized adaptive integrate and fire models.
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TrueNorth: Design and Tool Flow of a 65 mW 1 Million Neuron Programmable Neurosynaptic Chip
Filipp Akopyan,Jun Sawada,Andrew S. Cassidy,Rodrigo Alvarez-Icaza,John V. Arthur,Paul A. Merolla,Nabil Imam,Yutaka Nakamura,Pallab Datta,Gi-Joon Nam,Brian Taba,Michael P. Beakes,Bernard Brezzo,Jente B. Kuang,Rajit Manohar,William P. Risk,Bryan L. Jackson,Dharmendra S. Modha +17 more
TL;DR: This work developed TrueNorth, a 65 mW real-time neurosynaptic processor that implements a non-von Neumann, low-power, highly-parallel, scalable, and defect-tolerant architecture, and successfully demonstrated the use of TrueNorth-based systems in multiple applications, including visual object recognition.
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Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations
Ben Varkey Benjamin,Peiran Gao,Emmett McQuinn,Swadesh Choudhary,Anand R. Chandrasekaran,Jean-Marie Bussat,Rodrigo Alvarez-Icaza,John V. Arthur,Paul A. Merolla,Kwabena Boahen +9 more
TL;DR: Neurogrid as discussed by the authors is a real-time neuromorphic system for simulating large-scale neural models in real time using 16 Neurocores, including axonal arbor, synapse, dendritic tree, and soma.
Journal ArticleDOI
Convolutional networks for fast, energy-efficient neuromorphic computing
Steven K. Esser,Paul A. Merolla,John V. Arthur,Andrew S. Cassidy,Rathinakumar Appuswamy,Alexander Andreopoulos,David Berg,Jeffrey L. McKinstry,Timothy Melano,R Davis,Carmelo di Nolfo,Pallab Datta,Arnon Amir,Brian Taba,Myron D. Flickner,Dharmendra S. Modha +15 more
TL;DR: This approach allows the algorithmic power of deep learning to be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer.