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3. A Neuromimetic Integrated Circuit for Interactive Real-Time Simulation

Author

Saïghi, Sylvain, Tomas, Jean, Bornat, Yannick, Renaud, Sylvie, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Mira, José, editor, and Álvarez, José R., editor

Published
2005
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8. Neural Network for ultra-low power and real time computation

Author

Meyer, Charly, Lewden, Pierre, Vincent, Adrien, Tomas, Jean, Saïghi, Sylvain, and Saighi, Sylvain

Subjects
[SPI.NANO] Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics, ComputingMilieux_MISCELLANEOUS
Published
2019

13. Learning through ferroelectric domain dynamics in solid-state synapses

Author

Boyn, Sören, Grollier, Julie, Lecerf, Gwendal, Xu, Bin, Locatelli, Nicolas, Fusil, Stéphane, Girod, Stéphanie, Carrétéro, Cécile, Garcia, Karin, Xavier, Stéphane, Tomas, Jean, Bellaiche, Laurent, Bibes, Manuel, Barthélémy, Agnès, Saïghi, Sylvain, Garcia, Vincent, Unité mixte de physique CNRS/Thales (UMPhy CNRS/THALES), Centre National de la Recherche Scientifique (CNRS)-THALES, Laboratoire de l'intégration, du matériau au système (IMS), Université Sciences et Technologies - Bordeaux 1-Institut Polytechnique de Bordeaux-Centre National de la Recherche Scientifique (CNRS), Department of Electronic Engineering (Xiamen University), Xiamen University, Thales Research & Technology France, THALES, Institute for nanosciences and engineering and physics departement [University of Arkansas], and University of Arkansas [Fayetteville]

Subjects
Ferroelectrics and multiferroics, Hardware_MEMORYSTRUCTURES, Time Factors, Quantitative Biology::Neurons and Cognition, Science, Iron, Computer Science::Neural and Evolutionary Computation, Article, Computer Science::Emerging Technologies, Electricity, Electronic devices, Neural Networks, Computer, [SPI.NANO]Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics
Abstract

In the brain, learning is achieved through the ability of synapses to reconfigure the strength by which they connect neurons (synaptic plasticity). In promising solid-state synapses called memristors, conductance can be finely tuned by voltage pulses and set to evolve according to a biological learning rule called spike-timing-dependent plasticity (STDP). Future neuromorphic architectures will comprise billions of such nanosynapses, which require a clear understanding of the physical mechanisms responsible for plasticity. Here we report on synapses based on ferroelectric tunnel junctions and show that STDP can be harnessed from inhomogeneous polarization switching. Through combined scanning probe imaging, electrical transport and atomic-scale molecular dynamics, we demonstrate that conductance variations can be modelled by the nucleation-dominated reversal of domains. Based on this physical model, our simulations show that arrays of ferroelectric nanosynapses can autonomously learn to recognize patterns in a predictable way, opening the path towards unsupervised learning in spiking neural networks., Nature Communications, 8, ISSN:2041-1723

Published
2017
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14. ERP et conduite des changements : Alignement, sélection et déploiement

Author

Tomas, Jean-Louis, Gal, Yossi, Tomas, Jean-Louis, Tomas, Jean-Louis, Gal, Yossi, and Tomas, Jean-Louis

Abstract

De plus en plus d'entreprises migrent leurs applications informatiques internes vers des grands "progiciels intégrés" (ERP) qui offrent des solutions transversales homogènes, intégrées et évolutives. Cet ouvrage analyse le contexte et les enjeux de ce mouvement, illustre cette démarche à l'aide d'exemples concrets et analyse les facteurs-clé de réussite du choix, du déploiement et de l'utilisation opérationnelle d'un ERP. Cette sixième édition actualise les chiffres et les positions du marché. Elle met l'accent sur la conduite du changement.

Published
2011

15. Biomimetic neural networks for hybrid experiments

Author

Levi, Timothée, Ambroise, Matthieu, Grassia, Filippo, Malot, Olivia, Saighi, Sylvain, Bornat, Yannick, Tomas, Jean, Renaud, Sylvie, and Levi, Timothée

Subjects
[SDV.IB] Life Sciences [q-bio]/Bioengineering, ComputingMilieux_MISCELLANEOUS
Published
2014

16. Plasticity in memristive devices for spiking neural networks

Author

Saïghi, Sylvain, Mayr, Christian G., Serrano Gotarredona, María Teresa, Schmidt, Heidemarie, Lecerf, Gwendal, Tomas, Jean, Grollier, Julie, Boyn, Sören, Vincent, Adrien F., Querlioz, Damien, La Barbera, Selina, Alibart, Fabien, Vuillaume, Dominique, Bichler, Olivier, Gamrat, Christian, Linares Barranco, Bernabé, Saïghi, Sylvain, Mayr, Christian G., Serrano Gotarredona, María Teresa, Schmidt, Heidemarie, Lecerf, Gwendal, Tomas, Jean, Grollier, Julie, Boyn, Sören, Vincent, Adrien F., Querlioz, Damien, La Barbera, Selina, Alibart, Fabien, Vuillaume, Dominique, Bichler, Olivier, Gamrat, Christian, and Linares Barranco, Bernabé

Abstract

Memristive devices present a new device technology allowing for the realization of compact non-volatile memories. Some of them are already in the process of industrialization. Additionally, they exhibit complex multilevel and plastic behaviors, which make them good candidates for the implementation of artificial synapses in neuromorphic engineering. However, memristive effects rely on diverse physical mechanisms, and their plastic behaviors differ strongly from one technology to another. Here, we present measurements performed on different memristive devices and the opportunities that they provide. We show that they can be used to implement different learning rules whose properties emerge directly from device physics: real time or accelerated operation, deterministic or stochastic behavior, long term or short term plasticity. We then discuss how such devices might be integrated into a complete architecture. These results highlight that there is no unique way to exploit memristive devices in neuromorphic systems. Understanding and embracing device physics is the key for their optimal use

Published
2015

17. Plasticity in memristive devices for spiking neural networks

Author

European Commission, Saïghi, Sylvain, Mayr, Christian G., Serrano-Gotarredona, Teresa, Schmidt, Heidemarie, Lecerf, Gwendal, Tomas, Jean, Grollier, Julie, Boyn, Sören, Vincent, Adrien F., Querlioz, Damien, La Barbera, Selina, Alibart, Fabien, Vuillaume, Dominique, Bichler, Olivier, Gamrat, Christian, Linares-Barranco, Bernabé, European Commission, Saïghi, Sylvain, Mayr, Christian G., Serrano-Gotarredona, Teresa, Schmidt, Heidemarie, Lecerf, Gwendal, Tomas, Jean, Grollier, Julie, Boyn, Sören, Vincent, Adrien F., Querlioz, Damien, La Barbera, Selina, Alibart, Fabien, Vuillaume, Dominique, Bichler, Olivier, Gamrat, Christian, and Linares-Barranco, Bernabé

Abstract

Memristive devices present a new device technology allowing for the realization of compact non-volatile memories. Some of them are already in the process of industrialization. Additionally, they exhibit complex multilevel and plastic behaviors, which make them good candidates for the implementation of artificial synapses in neuromorphic engineering. However, memristive effects rely on diverse physical mechanisms, and their plastic behaviors differ strongly from one technology to another. Here, we present measurements performed on different memristive devices and the opportunities that they provide. We show that they can be used to implement different learning rules whose properties emerge directly from device physics: real time or accelerated operation, deterministic or stochastic behavior, long term or short term plasticity. We then discuss how such devices might be integrated into a complete architecture. These results highlight that there is no unique way to exploit memristive devices in neuromorphic systems. Understanding and embracing device physics is the key for their optimal use

Published
2015

20. Plasticity in memristive devices for spiking neural networks

Author

Saïghi, Sylvain, primary, Mayr, Christian G., additional, Serrano-Gotarredona, Teresa, additional, Schmidt, Heidemarie, additional, Lecerf, Gwendal, additional, Tomas, Jean, additional, Grollier, Julie, additional, Boyn, Sören, additional, Vincent, Adrien F., additional, Querlioz, Damien, additional, La Barbera, Selina, additional, Alibart, Fabien, additional, Vuillaume, Dominique, additional, Bichler, Olivier, additional, Gamrat, Christian, additional, and Linares-Barranco, Bernabé, additional

Published
2015
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22. Guaranteeing Spike Arrival Time in Multiboard & Multichip Spiking Neural Networks

Author

Belhadj, Bilel, Tomas, Jean, Malot, Olivia, Bornat, Yannick, N'Kaoua, Gilles, Renaud, Sylvie, and Plumejeau, Chrystel

Subjects
neural network, multiboard system, token passing communication, FPGA, [SPI.TRON] Engineering Sciences [physics]/Electronics
Abstract

Large-scale spiking neural networks (SNN) are generally run on distributed and parallel architectures with multiple computation nodes. These architectures induce extra delays due to the node-to-node communication process. In multiboard & multichip SNNs, important delays may affect spike arrival time and, thus, can alter simulation results. In this work, we propose a method aiming to guarantee spike arrival time with arbitrary prefixed deadlines. The communication architecture is based on the token-passing access policy to grant access to shared communication channels. We show that several network parameters must be set carefully if spikes have to meet their deadlines. Parameters are chosen by taking into account the communication channel bandwidth, the arbitrary deadlines and the worst case situation that can happen in generating neural activity in SNNs. As proof of concept, we have built a system that emulates up to 120 analog Hodgkin-Huxley neurons spread across 6 boards. Experimental results show that whatever it happens (unless there is a network fault), spikes reach their destination with a maximum delay of 5 microseconds.

Published
2010

23. A CMOS Resizing Methodology for Analog Circuits: linear and non-linear applications

Author

Levi, Timothée, Tomas, Jean, Lewis, Noëlle, Fouillat, Pascal, and Saighi, Sylvain

Subjects
[SPI.NANO] Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics, Hardware_INTEGRATEDCIRCUITS, Hardware_PERFORMANCEANDRELIABILITY
Abstract

This article proposes a CMOS resizing methodology for analog circuits during a technology migration. The scaling rules aim to be easy to apply and are based on the simplest MOS transistor model. This methodology represents a useful technique for resizing a design and may be used as a first-guess for an optimization procedure. The principle is to transpose one circuit topology from one technology to another, while keeping the main figures of merit, and the issue is to quickly calculate the new transistor dimensions. An optimization tool improves the results when it is necessary. This methodology is applied to both linear and non-linear examples: an OTA and a ring oscillator. The results are compared on four CMOS processes whose minimum length is respectively 0.8 m, 0.35 m, 0.25 m and 0.12 m

Published
2009

24. An IP-based library for the design of analog Hodgkin Huxley neurons

Author

Levi, Timothée, Lewis, N., Bornat, Yannick, Destexhe, Alain, Tomas, Jean, Saighi, Sylvain, and Plumejeau, Chrystel

Subjects
[SPI.NANO] Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics, [SPI.TRON] Engineering Sciences [physics]/Electronics, ComputingMilieux_MISCELLANEOUS
Published
2007

32. Silicon Integration of Biological Neurons Models

Author

Saighi, Sylvain, Tomas, Jean, Alvado, Ludovic, Bornat, Yannick, Renaud, Sylvie, and Import, Ims

Subjects
[SPI.NANO] Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics
Published
2003

33. L'enseignement des microsystèmes à Bordeaux: du 1er au 3ème cycle

Author

Dejous, Corinne, Dufour, Isabelle, Pellet, Claude, Rebiere, Dominique, Tomas, Jean, Import, Ims, Laboratoire de l'intégration, du matériau au système (IMS), Université Sciences et Technologies - Bordeaux 1-Institut Polytechnique de Bordeaux-Centre National de la Recherche Scientifique (CNRS), and Centre National de la Recherche Scientifique (CNRS)-Institut Polytechnique de Bordeaux-Université Sciences et Technologies - Bordeaux 1

Subjects
[SPI.NANO] Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics, [SPI.NANO]Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics
Published
2002

37. ERP et conduite des changements : Alignement, sélection et déploiement

Author

Gal, Yossi, Gal, Yossi, Tomas, Jean-Louis, Gal, Yossi, Gal, Yossi, and Tomas, Jean-Louis

Abstract

De plus en plus d'entreprises migrent leurs applications informatiques internes vers des grands "progiciels intégrés" (ERP) qui offrent des solutions transversales homogènes, intégrées et évolutives. Cet ouvrage analyse le contexte et les enjeux de ce mouvement, illustre cette démarche à l'aide d'exemples concrets et analyse les facteurs-clé de réussite du choix, du déploiement et de l'utilisation opérationnelle d'un ERP. Cette sixième édition actualise les chiffres et les positions du marché. Elle met l'accent sur la conduite du changement.

Published
2011

39. Simulations électriques des effets de débit de dose et de dose cumulée dans les circuits intégrés bipolaires : méthodologie de conception durcie aux radiations

Author

Deval, Yann, Briand, Renaud, Montagner, Xavier, Fouillat, Pascal, Tomas, Jean, Dom, Jean-Paul, Laboratoire de l'intégration, du matériau au système (IMS), Université Sciences et Technologies - Bordeaux 1-Institut Polytechnique de Bordeaux-Centre National de la Recherche Scientifique (CNRS), and Import, Ims

Subjects
[SPI.NANO] Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics, [SPI.NANO]Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics
Published
1996

40. Design For Testability of Built-In Laser Sensitive Cells

Author

Lapuyade, Hervé, Fouillat, Pascal, Maidon, Yvan, Tomas, Jean, Dom, Jean-Paul, Laboratoire de l'intégration, du matériau au système (IMS), Université Sciences et Technologies - Bordeaux 1-Institut Polytechnique de Bordeaux-Centre National de la Recherche Scientifique (CNRS), and Import, Ims

Subjects
[SPI.NANO] Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics, [SPI.NANO]Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics
Published
1995

46. A BiCMOS ASIC for modeling biological networks

Author

Le Masson, Sylvie, Deval, Yann, Le Masson, Gwendal, Tomas, Jean, Dupeyron, Denis, Dom, Jean-Paul, and Import, Ims

Subjects
[SPI.NANO] Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics
Published
1994

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