Proceedings Article10.1109/IJCNN.2013.6707077
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
- 01 Aug 2013
- pp 1-10
317
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.
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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. Judiciously balancing the dual objectives of functional capability and implementation/operational cost, we develop 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. Starting with the classic leaky integrate-and-fire neuron, we add: (a) configurable and reproducible stochasticity to the input, the state, and the output; (b) four leak modes that bias the internal state dynamics; (c) deterministic and stochastic thresholds; and (d) six reset modes for rich finite-state behavior. The model supports a wide variety of computational functions and neural codes. We capture 50+ neuron behaviors in a library for hierarchical composition of complex computations and behaviors. Although designed with cognitive algorithms and applications in mind, serendipitously, the neuron model can qualitatively replicate the 20 biologically-relevant behaviors of a dynamical neuron model.
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Citations
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.
1.5K
Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification.
Bodo Rueckauer,Iulia-Alexandra Lungu,Yuhuang Hu,Michael Pfeiffer,Michael Pfeiffer,Shih-Chii Liu +5 more
TL;DR: This paper shows conversion of popular CNN architectures, including VGG-16 and Inception-v3, into SNNs that produce the best results reported to date on MNIST, CIFAR-10 and the challenging ImageNet dataset.
Spiking Deep Convolutional Neural Networks for Energy-Efficient Object Recognition
TL;DR: A novel approach for converting a deep CNN into a SNN that enables mapping CNN to spike-based hardware architectures and evaluates the resulting SNN on publicly available Defense Advanced Research Projects Agency (DARPA) Neovision2 Tower and CIFAR-10 datasets and shows similar object recognition accuracy as the original CNN.
975
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
- 01 Jul 2017
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).
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.
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