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Andrew S. Cassidy
Researcher at IBM
Publications - 90
Citations - 7523
Andrew S. Cassidy is an academic researcher from IBM. The author has contributed to research in topics: TrueNorth & Neuromorphic engineering. The author has an hindex of 24, co-authored 90 publications receiving 5901 citations. Previous affiliations of Andrew S. Cassidy include Carnegie Mellon University & University of Cyprus.
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Journal ArticleDOI
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
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.
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.
Proceedings ArticleDOI
Cognitive computing building block: A versatile and efficient digital neuron model for neurosynaptic cores
Andrew S. Cassidy,Paul A. Merolla,John V. Arthur,Steve K. Esser,Bryan L. Jackson,Rodrigo Alvarez-Icaza,Pallab Datta,Jun Sawada,Theodore M. Wong,Vitaly Feldman,Arnon Amir,Daniel D Ben Dayan Rubin,Filipp Akopyan,Emmett McQuinn,William P. Risk,Dharmendra S. Modha +15 more
TL;DR: A simple, digital, reconfigurable, versatile spiking neuron model that supports one-to-one equivalence between hardware and simulation and is implementable using only 1272 ASIC gates is developed.
Proceedings ArticleDOI
Cognitive computing programming paradigm: A Corelet Language for composing networks of neurosynaptic cores
Arnon Amir,Pallab Datta,William P. Risk,Andrew S. Cassidy,Jeffrey A. Kusnitz,Steve K. Esser,Alexander Andreopoulos,Theodore M. Wong,Myron D. Flickner,Rodrigo Alvarez-Icaza,Emmett McQuinn,Ben Shaw,Norm Pass,Dharmendra S. Modha +13 more
TL;DR: A new programming paradigm that permits construction of complex cognitive algorithms and applications while being efficient for TrueNorth and effective for programmer productivity is developed.