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
TLDR
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.Abstract:
Inspired by the brain’s structure, we have developed an efficient, scalable, and flexible non–von Neumann architecture that leverages contemporary silicon technology. To demonstrate, we built a 5.4-billion-transistor chip with 4096 neurosynaptic cores interconnected via an intrachip network that integrates 1 million programmable spiking neurons and 256 million configurable synapses. Chips can be tiled in two dimensions via an interchip communication interface, seamlessly scaling the architecture to a cortexlike sheet of arbitrary size. The architecture is well suited to many applications that use complex neural networks in real time, for example, multiobject detection and classification. With 400-pixel-by-240-pixel video input at 30 frames per second, the chip consumes 63 milliwatts.read more
Citations
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Journal ArticleDOI
Event-Based Backpropagation can compute Exact Gradients for Spiking Neural Networks
TL;DR: In this paper, the authors derived the backpropagation algorithm for a continuous-time spiking neural network and a general loss function by applying the adjoint method together with the proper partial derivative jumps, allowing for backpropaggregation through discrete spike events without approximations.
Journal ArticleDOI
Organic materials and devices for brain-inspired computing: From artificial implementation to biophysical realism
TL;DR: An overview of the basic functional operation of the brain and its artificial counterparts, with a particular focus on organic materials and devices.
Journal ArticleDOI
Ferroelectric-based synapses and neurons for neuromorphic computing
TL;DR:
Proceedings ArticleDOI
Phase transition oxide neuron for spiking neural networks
Matthew Jerry,Wei-Yu Tsai,Baihua Xie,Xueqing Li,Vijay Narayanan,Arijit Raychowdhury,Suman Datta +6 more
TL;DR: The abrupt insulator-to-metal transition (IMT) in a prototypical IMT material, vanadium dioxide (VO2) is harnessed to experimentally demonstrate a compact integrate and fire spiking neuron and multiple spiking dynamics of the neuron relevant to implementing `winner take all' max pooling layers employed in image processing pipelines.
Journal ArticleDOI
Indium–gallium–zinc–oxide thin-film transistors: Materials, devices, and applications
TL;DR: The recent progress and major trends in the field of amorphous indium-gallium-zincoxide (IGZO) based thin-film transistors (TFTs) are reviewed in this article.
References
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Receptive fields, binocular interaction and functional architecture in the cat's visual cortex
David H. Hubel,Torsten N. Wiesel +1 more
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Journal ArticleDOI
Can programming be liberated from the von Neumann style?: a functional style and its algebra of programs
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Object vision and spatial vision: two cortical pathways
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Neuronal circuits of the neocortex
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