Neuromorphic Silicon Neuron Circuits

Indiveri, Giacomo; Linares-Barranco, B.; Hamilton, Tara Julia; Schaik, André van; Etienne‐Cummings, Ralph; Delbrück, Tobi; Liu, Shih-Chii; Dudek, Piotr; Häfliger, Philipp; Renaud, Sylvie; Schemmel, Johannes; Cauwenberghs, Gert; Arthur, John V.; Hynna, Kai M.; Folowosele, Fopefolu; Saïghi, Sylvain; Serrano-Gotarredona, Teresa; Wijekoon, Jayawan; Wang, YingXue; Boahen, Kwabena

doi:10.3389/fnins.2011.00073

Cited by 1,177 publications

(876 citation statements)

References 104 publications

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“…Fig.14 shows a hypothetical engineering system using some of the above tools (from [47,25]). -Moving object recognition ( fig.…”

Section: Snn Software and Hardware Implementations To Support Stprmentioning

confidence: 99%

“…-jAER (http://jaer.wiki.sourceforge.net) [23]; -Software simulators, such as Brian [16], Nestor, NeMo [79],etc; -Silicon retina camera [23]; -Silicon cochlea [107]; -SNN hardware realisation of LIFM and SDSP [47][48][49][50]; -The SpiNNaker hardware/software environment [89,116]; -FPGA implementations of SNN [56]; -The IBM LIF SNN chip, recently announced. Fig.14 shows a hypothetical engineering system using some of the above tools (from [47,25]).…”

Section: Snn Software and Hardware Implementations To Support Stprmentioning

confidence: 99%

See 1 more Smart Citation

Evolving Spiking Neural Networks and Neurogenetic Systems for Spatio- and Spectro-Temporal Data Modelling and Pattern Recognition

Kasabov

2012

Advances in Computational Intelligence

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“…Fig.14 shows a hypothetical engineering system using some of the above tools (from [47,25]). -Moving object recognition ( fig.…”

Section: Snn Software and Hardware Implementations To Support Stprmentioning

confidence: 99%

Section: Snn Software and Hardware Implementations To Support Stprmentioning

confidence: 99%

Evolving Spiking Neural Networks and Neurogenetic Systems for Spatio- and Spectro-Temporal Data Modelling and Pattern Recognition

Kasabov

2012

Advances in Computational Intelligence

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“…While the CMOS based neurons are nowadays commercially available, 1 the appropriate candidate for the synapse is still under discussion. There are two main possible ways of synapse realization: a digital one (e.g., as the Static Random Access Memory 2 or floating gate transistor 3 ) and an analogue one (memristive device). 4 The main advantage of the first one is its full integration with the standard CMOS technology.…”

Section: Introductionmentioning

confidence: 99%

First steps towards the realization of a double layer perceptron based on organic memristive devices

et al. 2016

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Memristors are widely considered as promising elements for the efficient implementation of synaptic weights in artificial neural networks (ANNs) since they are resistors that keep memory of their previous conductive state. Whereas demonstrations of simple neural networks (e.g., a single-layer perceptron) based on memristors already exist, the implementation of more complicated networks is more challenging and has yet to be reported. In this study, we demonstrate linearly nonseparable combinational logic classification (XOR logic task) using a network implemented with CMOS-based neurons and organic memrisitive devices that constitutes the first step toward the realization of a double layer perceptron. We also show numerically the ability of such network to solve a principally analogue task which cannot be realized by digital devices. The obtained results prove the possibility to create a multilayer ANN based on memristive devices that paves the way for designing a more complex network such as the double layer perceptron.

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“…The optoelectronic hardware platform is predicted to achieve 20 aJ/synapse event. By comparison, many CMOS systems are on the order of 20 pJ/synapse event [10,25,26].…”

Section: Introductionmentioning

confidence: 99%

Superconducting Optoelectronic Circuits for Neuromorphic Computing

et al. 2017

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Neural networks have proven effective for solving many difficult computational problems. Implementing complex neural networks in software is very computationally expensive. To explore the limits of information processing, it will be necessary to implement new hardware platforms with large numbers of neurons, each with a large number of connections to other neurons. Here we propose a hybrid semiconductor-superconductor hardware platform for the implementation of neural networks and large-scale neuromorphic computing. The platform combines semiconducting few-photon light-emitting diodes with superconducting-nanowire single-photon detectors to behave as spiking neurons. These processing units are connected via a network of optical waveguides, and variable weights of connection can be implemented using several approaches. The use of light as a signaling mechanism overcomes fanout and parasitic constraints on electrical signals while simultaneously introducing physical degrees of freedom which can be employed for computation. The use of supercurrents achieves the low power density necessary to scale to systems with enormous entropy. The proposed processing units can operate at speeds of at least 20 MHz with fully asynchronous activity, light-speed-limited latency, and power densities on the order of 1 mW/cm 2 for neurons with 700 connections operating at full speed at 2 K. The processing units achieve an energy efficiency of ≈ 20 aJ per synapse event. By leveraging multilayer photonics with deposited waveguides and superconductors with feature sizes > 100 nm, this approach could scale to systems with massive interconnectivity and complexity for advanced computing as well as explorations of information processing capacity in systems with an enormous number of information-bearing microstates.

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Neuromorphic Silicon Neuron Circuits

Cited by 1,177 publications

References 104 publications

Evolving Spiking Neural Networks and Neurogenetic Systems for Spatio- and Spectro-Temporal Data Modelling and Pattern Recognition

Evolving Spiking Neural Networks and Neurogenetic Systems for Spatio- and Spectro-Temporal Data Modelling and Pattern Recognition

First steps towards the realization of a double layer perceptron based on organic memristive devices

Superconducting Optoelectronic Circuits for Neuromorphic Computing

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