Utilisation des nano-composants électroniques dans les architectures de traitement associées aux imageurs

By using learning mechanisms extracted from recent discoveries in neuroscience, spiking neural networks have demonstrated their ability to efficiently analyze the large amount of data from our environment. The implementation of such circuits on conventional processors does not allow the efficient exploitation of their parallelism. The use of digital memory to implement the synaptic weight does not allow the parallel reading or the parallel programming of the synapses and it is limited by the bandwidth of the connection between the memory and the processing unit. Emergent memristive memory technologies could allow implementing this parallelism directly in the heart of the memory.In this thesis, we consider the development of an embedded spiking neural network based on emerging memory devices. First, we analyze a spiking network to optimize its different components: the neuron, the synapse and the STDP learning mechanism for digital implementation. Then, we consider implementing the synaptic memory with emergent memristive devices. Finally, we present the development of a neuromorphic chip co-integrating CMOS neurons with CBRAM synapses.En utilisant les méthodes d’apprentissages tirées des récentes découvertes en neuroscience, les réseaux de neurones impulsionnels ont démontrés leurs capacités à analyser efficacement les grandes quantités d’informations provenant de notre environnement. L’implémentation de ces circuits à l’aide de processeurs classiques ne permet pas d’exploiter efficacement leur parallélisme. L’utilisation de mémoire numérique pour implémenter les poids synaptique ne permet pas la lecture ou la programmation parallèle des synapses et est limité par la bande passante reliant la mémoire à l’unité de calcul. Les technologies mémoire de type memristive pourrait permettre l’implémentation de ce parallélisme au coeur de la mémoire.Dans cette thèse, nous envisageons le développement d’un réseau de neurones impulsionnels dédié au monde de l’embarqué à base de dispositif mémoire émergents. Dans un premier temps, nous avons analysé un réseau impulsionnel afin d’optimiser ses différentes composantes : neurone, synapse et méthode d’apprentissage STDP en vue d’une implémentation numérique. Dans un second temps, nous envisageons l’implémentation de la mémoire synaptique par des dispositifs memristifs. Enfin, nous présentons le développement d’une puce co-intégrant des neurones implémentés en CMOS avec des synapses en technologie CBRAM

Les microplastiques en milieu marin : supports de contaminants chimiques - Étude bibliographique

microplastics are ubiquitous in the marine environment, particularly in ocean gyres. this review makes an assessment of the current knowledge about microplastics as support of chemical contaminants in marine environment. 121 papers were included in this report, with 53 on the theme "microplastics, contaminants and marine environment". adsorption and desorption capacities of contaminants on microplastic depend on the contaminant, the microplastic, and external factors (salinity, organic matter and interaction with other contaminants). regardless of the sampled matrix (water and sand) and the geographical area, results have shown the presence of chemical contaminants on microplastics, which are persistent organic pollutants (pops) in the water (pcbs, pahs, pesticides) or plastic plastic additives (pbde, bisphenol a, alkylphenols). a large variability in concentrations is observed according to the place, the type of plastic and between each microplastic. the different data acquisition methods do not allow the comparison between studies. in biota, desorption of contaminants on microplastics is favored in the presence of surfactants. it has been shown that once ingested, microplastic can transfer certain contaminants. the fish exposed to microplastics with pyrene, has different effects compared to fish exposed only to pyrene. microplastics concentrate pollutants, transport them and increase their persistence in the marine environment. with microplastics, exposure of biota to chemical contaminants is increased. microplastics distort risk of contamination for biota and the effects of contaminants. the role of microplastics in the total load of contaminants in marine organisms is not yet well defined, the difficulty is to know if these contaminants come from environment or microplastics. the accumulation and biomagnification of contaminants in organisms via microplastics remain to be studied.Les microplastiques sont omniprésents dans l’environnement marin, notamment dans les gyres océaniques. Cette étude bibliographique réalise un bilan des connaissances actuelles sur les microplastiques comme support de contaminants chimiques dans le milieu marin. 121 documents ont été pris en compte dans ce rapport dont 53 sur la thématique « microplastiques, contaminants et milieu marin ». Les capacités d’adsorption/désorption des contaminants sur les microplastiques dépendent du contaminant, du support microplastique, et de facteurs externes (salinité, matière organique et interaction avec les autres contaminants). Quelle que soit la matrice échantillonnée (eau et sable) et la zone géographique considérée, les résultats mettent en évidence la présence de contaminants chimiques sur les microplastiques, que ce soit des polluants organiques persistants (POP) présent dans l’eau (PCB, HAP, pesticides) ou des additifs du plastique (PBDE, bisphénol A, alkylphénols). Une grande variabilité des concentrations est observée en fonction des lieux, des supports et entre chaque microplastique. Les différentes méthodes d’acquisition des données ne permettent pas la comparaison des études entre elles. Au niveau des organismes, la désorption des contaminants sur les microplastiques est favorisée en présence d’agents tensioactifs. Il a ainsi été démontré qu’une fois ingérés, les microplastiques peuvent transférer certains contaminants aux organismes. L’exposition de poissons à des microplastiques avec du pyrène, présente des effets différents par rapport aux poissons exposés au pyrène seul. Les microplastiques concentrent les polluants, les transportent et augmentent leur persistance dans l’environnement marin. Ils augmentent l’exposition des organismes aux contaminants chimiques et modifient leur risque de contamination et les effets des contaminants. Le rôle des microplastiques dans la charge totale en contaminants dans les organismes marins n’est pas encore bien défini, la difficulté étant de savoir si ces contaminants viennent de l’environnement ou des microplastiques. L’accumulation et la bioamplification des contaminants dans les organismes via les microplastiques restent à étudier

Integration of memory nano-devices in image sensors processing architecture

En utilisant les méthodes d’apprentissages tirées des récentes découvertes en neuroscience, les réseaux de neurones impulsionnels ont démontrés leurs capacités à analyser efficacement les grandes quantités d’informations provenant de notre environnement. L’implémentation de ces circuits à l’aide de processeurs classiques ne permet pas d’exploiter efficacement leur parallélisme. L’utilisation de mémoire numérique pour implémenter les poids synaptique ne permet pas la lecture ou la programmation parallèle des synapses et est limité par la bande passante reliant la mémoire à l’unité de calcul. Les technologies mémoire de type memristive pourrait permettre l’implémentation de ce parallélisme au coeur de la mémoire.Dans cette thèse, nous envisageons le développement d’un réseau de neurones impulsionnels dédié au monde de l’embarqué à base de dispositif mémoire émergents. Dans un premier temps, nous avons analysé un réseau impulsionnel afin d’optimiser ses différentes composantes : neurone, synapse et méthode d’apprentissage STDP en vue d’une implémentation numérique. Dans un second temps, nous envisageons l’implémentation de la mémoire synaptique par des dispositifs memristifs. Enfin, nous présentons le développement d’une puce co-intégrant des neurones implémentés en CMOS avec des synapses en technologie CBRAM.By using learning mechanisms extracted from recent discoveries in neuroscience, spiking neural networks have demonstrated their ability to efficiently analyze the large amount of data from our environment. The implementation of such circuits on conventional processors does not allow the efficient exploitation of their parallelism. The use of digital memory to implement the synaptic weight does not allow the parallel reading or the parallel programming of the synapses and it is limited by the bandwidth of the connection between the memory and the processing unit. Emergent memristive memory technologies could allow implementing this parallelism directly in the heart of the memory.In this thesis, we consider the development of an embedded spiking neural network based on emerging memory devices. First, we analyze a spiking network to optimize its different components: the neuron, the synapse and the STDP learning mechanism for digital implementation. Then, we consider implementing the synaptic memory with emergent memristive devices. Finally, we present the development of a neuromorphic chip co-integrating CMOS neurons with CBRAM synapses

Memristive based device arrays combined with Spike based coding can enable efficient implementations of embedded neuromorphic circuits

Conference of 61st IEEE International Electron Devices Meeting, IEDM 2015 ; Conference Date: 7 December 2015 Through 9 December 2015; Conference Code:119534International audienceSince the rapid development of post-CMOS technologies in the last decade, there has been a growing interest in utilizing them for implementing neuromorphic or brain-like computing machines. Besides attempts to build realistic circuits that would mimic the functioning of biological neurons as close as possible [1][2], our team is focused on implementing neuromorphic circuits suitable for embedded applications. This objective puts the emphasis on two majors concerns: integration and energy efficiency. In our quest for ultimate integration, we first report on investigating for the best synapse-like technology among the realm of potential candidates. We then report our investigations on the feasibility of large crossbars of synapse-like devices and show that there is still a long way ahead. Finally in an effort to tackle the energy problem, we introduce spike based coding for deep neuromorphic architectures and discuss our argument that spike coding combined with memristive synaptic devices could pave the way for future embedded neuromorphic circuits

Design exploration methodology for memristor-based spiking neuromorphic architectures with the Xnet event-driven simulator

International audience—We introduce an event-based methodology, and its accompanying simulator (" Xnet ") for memristive nanodevice-based neuromorphic hardware, which aims to provide an intermediate modeling level, between low-level hardware description languages and high-level neural networks simulators used primarily in neurosciences. This simulator was used to establish several results on Spike-Timing-Dependent Plasticity (STDP) modeling and implementation with Resistive RAM (RRAM), Conductive Bridge RAM (CBRAM) and Phase-Change Memory (PCM) type of memristive nanodevices. We present several simulation case studies that illustrate the event-based simulation strategies that we implemented, including unsupervised features extraction and Monte Carlo simulations. A discussion comparing event-based and fixed time-step simulation is included as well, and gives some metrics to guide the choice between the two depending on the application to simulate

Sneak paths effects in CBRAM memristive devices arrays for spiking neural networks

Conference of 2014 IEEE/ACM International Symposium on Nanoscale Architectures, NANOARCH 2014 ; Conference Date: 8 July 2014 Through 10 July 2014; Conference Code:107210International audienceIn this paper, we study the effects of sneak paths and parasitic metal line resistance in arrays of CBRAM memristive devices operating as synapses for spiking neural networks. Three structures of crosspoint array are reviewed: the crossbar (1R), the anode connected matrix (1T-IR) and the cathode connected matrix (1T-IR). We show that the crossbar is an energy-consuming structure with high leakage during SET/RESET and with an increased switching time due to voltage drops along the lines. Furthermore, we show that parasitic line resistance can have a significant impact on the read resistance of the devices, depending on their location in the crossbar

Design study of efficient digital order-based STDP neuron implementations for extracting temporal features

Conference of 2013 International Joint Conference on Neural Networks, IJCNN 2013 ; Conference Date: 4 August 2013 Through 9 August 2013; Conference Code:102436International audienceSpiking neural networks are naturally asynchronous and use pulses to carry information. In this paper, we consider implementing such networks on a digital chip. We used an event-based simulator and we started from a previously established simulation, which emulates an analog spiking neural network, that can extract complex and overlapping, temporally correlated features. We modified this simulation to allow an easier integration in an embedded digital implementation. We first show that a four bits synaptic weight resolution is enough to achieve the best performance, although the network remains functional down to a 2 bits weight resolution. Then we show that a linear leak could be implemented to simplify the neurons leakage calculation. Finally, we demonstrate that an order-based STDP with a fixed number of potentiated synapses as low as 200 is efficient for features extraction. A simulation including these modifications, which lighten and increase the efficiency of digital spiking neural network implementation shows that the learning behavior is not affected, with a recognition rate of 98% in a cars trajectories detection application