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
- pp 1-10
Reads0
Chats0
TLDR
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.Abstract:
Marching along the DARPA SyNAPSE roadmap, IBM unveils a trilogy of innovations towards the TrueNorth cognitive computing system inspired by the brain's function and efficiency. The sequential programming paradigm of the von Neumann architecture is wholly unsuited for TrueNorth. Therefore, as our main contribution, we develop a new programming paradigm that permits construction of complex cognitive algorithms and applications while being efficient for TrueNorth and effective for programmer productivity. The programming paradigm consists of (a) an abstraction for a TrueNorth program, named Corelet, for representing a network of neurosynaptic cores that encapsulates all details except external inputs and outputs; (b) an object-oriented Corelet Language for creating, composing, and decomposing corelets; (c) a Corelet Library that acts as an ever-growing repository of reusable corelets from which programmers compose new corelets; and (d) an end-to-end Corelet Laboratory that is a programming environment which integrates with the TrueNorth architectural simulator, Compass, to support all aspects of the programming cycle from design, through development, debugging, and up to deployment. The new paradigm seamlessly scales from a handful of synapses and neurons to networks of neurosynaptic cores of progressively increasing size and complexity. The utility of the new programming paradigm is underscored by the fact that we have designed and implemented more than 100 algorithms as corelets for TrueNorth in a very short time span.read more
Citations
More filters
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.
Posted Content
A Survey of Neuromorphic Computing and Neural Networks in Hardware.
Catherine D. Schuman,Thomas E. Potok,Robert M. Patton,J. Douglas Birdwell,Mark Edward Dean,Garrett S. Rose,James S. Plank +6 more
TL;DR: An exhaustive review of the research conducted in neuromorphic computing since the inception of the term is provided to motivate further work by illuminating gaps in the field where new research is needed.
Proceedings ArticleDOI
A Low Power, Fully Event-Based Gesture Recognition System
Arnon Amir,Brian Taba,David Berg,Timothy Melano,Jeffrey L. McKinstry,Carmelo di Nolfo,Tapan K. Nayak,Alexander Andreopoulos,Guillaume Garreau,Marcela Mendoza,Jeff Kusnitz,Michael DeBole,Steve K. Esser,Tobi Delbruck,Myron D. Flickner,Dharmendra S. Modha +15 more
TL;DR: This work presents the first gesture recognition system implemented end-to-end on event-based hardware, using a TrueNorth neurosynaptic processor to recognize hand gestures in real-time at low power from events streamed live by a Dynamic Vision Sensor (DVS).
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.
References
More filters
Journal ArticleDOI
Can programming be liberated from the von Neumann style?: a functional style and its algebra of programs
TL;DR: A new class of computing systems uses the functional programming style both in its programming language and in its state transition rules; these systems have semantics loosely coupled to states—only one state transition occurs per major computation.
Book
The Connection Machine
TL;DR: The Connection Machine describes a fundamentally different kind of computer that Daniel Hillis and others are now developing to perform tasks that no conventional, sequential machine can solve in a reasonable time.
Book
Things That Make Us Smart: Defending Human Attributes In The Age Of The Machine
TL;DR: This book discusses a Human-Centered Technology, Experiencing the World, and the Power of Representation, as well as Distributed Cognition and Soft and Hard Technology.
Journal ArticleDOI
PyNN: A Common Interface for Neuronal Network Simulators.
Andrew P. Davison,Daniel Brüderle,Jochen Martin Eppler,Jens Kremkow,Eilif Muller,Dejan Pecevski,Laurent Perrinet,Pierre Yger +7 more
TL;DR: PyNN increases the productivity of neuronal network modelling by providing high-level abstraction, by promoting code sharing and reuse, and by providing a foundation for simulator-agnostic analysis, visualization and data-management tools.
Book
The Designer's Guide to VHDL
TL;DR: The Designer's Guide to VHDL is both a comprehensive manual for the language and an authoritative reference on its use in hardware design at all levels, from the system level to the gate level.
Related Papers (5)
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
TrueNorth: Design and Tool Flow of a 65 mW 1 Million Neuron Programmable Neurosynaptic Chip
Convolutional networks for fast, energy-efficient neuromorphic computing
Loihi: A Neuromorphic Manycore Processor with On-Chip Learning
Michael Davies,Narayan Srinivasa,Tsung-Han Lin,Gautham N. Chinya,Cao Yongqiang,Sri Harsha Choday,Georgios D. Dimou,Prasad Joshi,Nabil Imam,Shweta Jain,Yuyun Liao,Chit-Kwan Lin,Andrew Lines,Ruokun Liu,Deepak A. Mathaikutty,Steven McCoy,Arnab Paul,Jonathan Tse,Guruguhanathan Venkataramanan,Yi-Hsin Weng,Andreas Wild,Yoon Seok Yang,Hong Wang +22 more