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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Posted Content
Going Deeper With Directly-Trained Larger Spiking Neural Networks
TL;DR: A threshold-dependent batch normalization (tdBN) method based on the emerging spatio-temporal backpropagation, termed "STBP-tdBN", enabling direct training of a very deep SNN and the efficient implementation of its inference on neuromorphic hardware is proposed.
Proceedings ArticleDOI
Conversion of analog to spiking neural networks using sparse temporal coding
Bodo Rueckauer,Shih-Chii Liu +1 more
TL;DR: This work presents an efficient temporal encoding scheme, where the analog activation of a neuron in the ANN is treated as the instantaneous firing rate given by the time-to-first-spike (TTFS) in the converted SNN.
Journal ArticleDOI
Machine learning approach to OAM beam demultiplexing via convolutional neural networks.
Timothy Doster,Abbie T. Watnik +1 more
TL;DR: This work proposes a technique to demultiplex these OAM-carrying beams by capturing an image of the unique multiplexing intensity pattern and training a convolutional neural network (CNN) as a classifier, which allows for simplicity of operation as alignment is unnecessary, orthogonality constraints are loosened, and costly optical hardware is not required.
Journal ArticleDOI
Training Deep Spiking Convolutional Neural Networks With STDP-Based Unsupervised Pre-training Followed by Supervised Fine-Tuning
TL;DR: This paper proposes a pre-training scheme using biologically plausible unsupervised learning, namely Spike-Timing-Dependent-Plasticity (STDP), in order to better initialize the parameters in multi-layer systems prior to supervised optimization.
Journal ArticleDOI
Memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity
Giacomo Pedretti,Valerio Milo,Stefano Ambrogio,Roberto Carboni,S. Bianchi,Alessandro Calderoni,Nirmal Ramaswamy,Alessandro S. Spinelli,Daniele Ielmini +8 more
TL;DR: Unsupervised learning of a static pattern and tracking of a dynamic pattern of up to 4 × 4 pixels are demonstrated, paving the way for intelligent hardware technology with up-scaled memristive neural networks.
References
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Journal ArticleDOI
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
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.
Journal ArticleDOI
Object vision and spatial vision: two cortical pathways
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Journal ArticleDOI
Modality and topographic properties of single neurons of cat's somatic sensory cortex.
TL;DR: Observations upon the modality and topographical attributes of single neurons of the first somatic sensory area of the cat’s cerebral cortex, the analogue of the cortex of the postcentral gyrus in the primate brain, support an hypothesis of the functional organization of this cortical area.
Journal ArticleDOI
Neuronal circuits of the neocortex
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