Novel devices are being investigated as artificial synapse candidates for neuromorphic computing. These memory devices share the characteristics of an electronic element called memristor. The memristor can be regarded as a resistor with a history dependent resistance, which mimics the plasticity of a biological synapse. The present work presents various types of candidate devices that have been proposed in neuromorphic research, describes how they mimic a biological synapse and how they can be employed in artificial neuron network architectures.

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The growing availability of UAVs has created a demand for drone detection systems. Several studies have used neuromorphic cameras to detect UAVs; however, a fully neuromorphic system remains to be explored. We present a fully neuromorphic system consisting of an event camera and a spiking neural network running on neuromorphic hardware. Two spiking neural network architectures have been evaluated and compared to a non-spiking artificial neural network. The spiking networks show promise and perform on par with the non-spiking network in a few scenarios. Spiking networks were deployed on the Synsense Speck, a neuromorphic system on a chip, and demonstrated increased performance compared to simulations. The deployed network is capable of detecting drones up to a distance of 20 meters with high probability while consuming less than 7.13 milliwatts. The system can operate for over a year powered by a small power bank. In contrast, the equivalent non-spiking network running on the NVIDIA Jetson would operate for a few hours. The use of neuromorphic hardware enables sustained UAV detection in remote and challenging environments previously deemed inaccessible due to power constraints.

Due to the voltage driven switching at low voltages combined with nonvolatility of the achieved polarization state, ferroelectric materials have a unique potential for low power nonvolatile electronic devices. The competitivity of such devices is hindered by compatibility issues of well-known ferroelectrics with established semiconductor technology. The discovery of ferroelectricity in hafnium oxide changed this situation. The natural application of nonvolatile devices is as a memory cell. Nonvolatile memory devices also built the basis for other applications like in-memory or neuromorphic computing. Three different basic ferroelectric devices can be constructed: ferroelectric capacitors, ferroelectric field effect transistors and ferroelectric tunneling junctions. In this article first the material science of the ferroelectricity in hafnium oxide will be summarized with a special focus on tailoring the switching characteristics towards different applications.The current status of nonvolatile ferroelectric memories then lays the ground for looking into applications like in-memory computing. Finally, a special focus will be given to showcase how the basic building blocks of spiking neural networks, the neuron and the synapse, can be realized and how they can be combined to realize neuromorphic computing systems. A summary, comparison with other technologies like resistive switching devices and an outlook completes the paper.

<div>Event-Based cameras, also known as neuromophic cameras or dynamic vision sensors, are an imaging modality that attempt to mimic human eyes by asynchronously measuring contrast over time. If the contrast changes sufficiently then a 1-bit event is output, indicating whether the contrast has gone up or down. This stream of events is sparse, and its asynchronous nature allows the pixels to have a high dynamic range and high temporal resolution. However, these events do not encode the intensity of the scene, resulting in an inverse problem to estimate intensity images from the event stream. Hybrid event-based cameras, such as the DAVIS camera, provide a reference intensity image that can be leveraged when estimating the intensity at each pixel during an event. Normally, inverse problems are solved by formulating a forward and prior model and minimizing the associated cost, however, for this problem, the Plug and Play (P&P) algorithm is used to solve the inverse problem. In this case, P&P replaces the prior model subproblem with a denoiser, making the algorithm modular, easier to implement. We propose an idealized forward model that assumes the contrast steps measured by the DAVIS camera are uniform in size to simplify the problem. We show that the algorithm can swiftly reconstruct the scene intensity at a user-specified frame rate, depending on the chosen denoiser’s computational complexity and the selected frame rate.</div>

This work has been supported by the European FACETS-ITN project. Within the frameworkof this project, we contribute to the simulation of cortical cell types (employingexperimental electrophysiological data of these cells as references), using a specific VLSIneural circuit to simulate, at the single cell level, the models studied as references in theFACETS project. The real-time intrinsic properties of the neuromorphic circuits, whichprecisely compute neuron conductance-based models, will allow a systematic and detailedexploration of the models, while the physical and analog aspect of the simulations, as opposedthe software simulation aspect, will provide inputs for the development of the neuralhardware at the network level. The second goal of this thesis is to contribute to the designof a mixed hardware-software platform (PAX), specifically designed to simulate spikingneural networks. The tasks performed during this thesis project included: 1) the methodsused to obtain the appropriate parameter sets of the cortical neuron models that can beimplemented in our analog neuromimetic chip (the parameter extraction steps was validatedusing a bifurcation analysis that shows that the simplified HH model implementedin our silicon neuron shares the dynamics of the HH model); 2) the fully customizablefitting method, in voltage-clamp mode, to tune our neuromimetic integrated circuits usinga metaheuristic algorithm; 3) the contribution to the development of the PAX systemin terms of software tools and a VHDL driver interface for neuron configuration in theplatform. Finally, it also addresses the issue of synaptic tuning for future SNN simulation.

La recherche dans le domaine de l’informatique neuro-inspirée suscite beaucoup d'intérêt depuis quelques années. Avec des applications potentielles dans des domaines tels que le traitement de données à grande échelle, la robotique ou encore les systèmes autonomes intelligents pour ne citer qu'eux, des paradigmes de calcul bio-inspirés sont étudies pour la prochaine génération solutions informatiques (post-Moore, non-Von Neumann) ultra-basse consommation. Dans ce travail, nous discutons les rôles que les différentes technologies de mémoire résistive non-volatiles émergentes (RRAM), notamment (i) Phase Change Memory (PCM), (ii) Conductive-Bridge Memory (CBRAM) et de la mémoire basée sur une structure Metal-Oxide (OXRAM) peuvent jouer dans des dispositifs neuromorphiques dédies. Nous nous concentrons sur l'émulation des effets de plasticité synaptique comme la potentialisation à long terme (Long Term Potentiation, LTP), la dépression à long terme (Long Term Depression, LTD) et la théorie STDP (Spike-Timing Dependent Plasticity) avec des synapses RRAM. Nous avons développé à la fois de nouvelles architectures de faiblement énergivore, des méthodologies de programmation ainsi que des règles d’apprentissages simplifiées inspirées de la théorie STDP spécifiquement optimisées pour certaines technologies RRAM. Nous montrons l’implémentation de systèmes neuromorphiques a grande échelle et efficace énergétiquement selon deux approches différentes: (i) des synapses multi-niveaux déterministes et (ii) des synapses stochastiques binaires. Des prototypes d'applications telles que l’extraction de schéma visuel et auditif complexe sont également montres en utilisant des réseaux de neurones impulsionnels (Feed-forward Spiking Neural Network, SNN). Nous introduisons également une nouvelle méthodologie pour concevoir des neurones stochastiques très compacts qui exploitent les caractéristiques physiques intrinsèques des appareils CBRAM. / Research in the field of neuromorphic- and cognitive- computing has generated a lot of interest in recent years. With potential application in fields such as large-scale data driven computing, robotics, intelligent autonomous systems to name a few, bio-inspired computing paradigms are being investigated as the next generation (post-Moore, non-Von Neumann) ultra-low power computing solutions. In this work we discuss the role that different emerging non-volatile resistive memory technologies (RRAM), specifically (i) Phase Change Memory (PCM), (ii) Conductive-Bridge Memory (CBRAM) and Metal-Oxide based Memory (OXRAM) can play in dedicated neuromorphic hardware. We focus on the emulation of synaptic plasticity effects such as long-term potentiation (LTP), long term depression (LTD) and spike-timing dependent plasticity (STDP) with RRAM synapses. We developed novel low-power architectures, programming methodologies, and simplified STDP-like learning rules, optimized specifically for some RRAM technologies. We show the implementation of large-scale energy efficient neuromorphic systems with two different approaches (i) deterministic multi-level synapses and (ii) stochastic-binary synapses. Prototype applications such as complex visual- and auditory- pattern extraction are also shown using feed-forward spiking neural networks (SNN). We also introduce a novel methodology to design low-area efficient stochastic neurons that exploit intrinsic physical effects of CBRAM devices.

Nous présentons dans ce manuscrit un neurone électronique expérimental basé sur le modèle complet de Morris-Lecar sans simplifications, afin d’obtenir une cellule de base pour étudier l’association collective de neurones couplés. La conception du circuit est donnée en détail selon les différents courants ioniques du modèle. Les résultats expérimentaux sont comparés aux prédictions théoriques, conduisant à un bon accord, ce qui valide donc notre circuit. Nous présentons les différents domaines de bifurcation selon les paramètres de contrôle, la capacité membranaire et le courant d’excitation. Nous avons mis en évidence le comportement du neurone pour chaque zone de paramétrage. Un couplage de ces neurones est introduit en utilisant des simulations Pspice (Multisim) où les neurones ont été conçus pour être les mêmes qu’expérimentalement. Premièrement, nous avons simulé une chaîne fermée de 26 neurones faiblement couplés le long de laquelle les ondes se propagent avec des phases en opposition 2 à 2. Pour cette première étude, on travaille dans une zone présentant uniquement un cycle limite stable. Deuxièmement, une dizaine de neurones sont couplés, avec un choix de paramètres correspondant à une deuxième zone où il y a deux attracteurs, un cycle limite stable et un point fixe stable, tandis qu’entre eux se trouve un cycle instable. Selon le nombre de neurones qui oscillent initialement et les conditions aux bords, nos études montrent que le système évolue vers un état où seuls 1, 2 ou 3 neurones restent à l’état oscillatoire, tandis que les autres sont retournés à un état de repos, ce qui met en évidence un phénomène de clusterisation. Il est à noter que certaines parties de notre circuit de base peuvent ainsi être utilisées dans d’autres modèles de neurones, car ces parties correspondent à la production des divers courants ioniques qu’on retrouve dans d’autres modèles. / We present in this manuscript an experimental electronic neuron based on complete Morris-Lecar model without simplifications, able to become an experimental unit tool to study collective association of robust coupled neurons. The circuit design is given in details according to the ionic currents of this model. The experimental results are compared with the theoretical prediction, leading to a good agreement between them, which therefore validates the circuit. We present the different areas according to the bifurcation control parameters, the membrane capacitance and the excitation current. We have highlighted the behavior of the neuron for each parameters zone. A coupling of such neurons is introduced by using Pspice simulations (Multisim) where neurons have been designed to be the same as the experimental one. First, we simulate a chain of up to 26 neurons weakly coupled along which anti-phase wave patterns propagate with phases in opposition 2 to 2. Second, about ten neurons are coupled, and we succeed to generate clusters based on the boundary conditions of theneurons and their initial conditions. For this study, we work in the region close to the fold bifurcation of limit cycles, where two limit cycles exist, one being stable and another one unstable. Our studies show that the system evolves to a state where only 1, 2 or 3 neurons remain in the oscillatory state, while others returned to a state of rest, which highlights a phenomenon of clustering. The use of some parts of the circuit is also possible for other neuron models, namely for those based on ionic currents.

L'augmentation du parallélisme, sur des puces dont la densité d'intégration est en constante croissance, soulève un certain nombre de défis tels que le routage de l'information qui se confronte au problème de "goulot d'étranglement de données", ou la simple difficulté à exploiter un parallélisme massif et grandissant avec les paradigmes de calcul modernes issus pour la plupart, d'un historique séquentiel.Nous nous inscrivons dans une démarche bio-inspirée pour définir un nouveau type d'architecture, basée sur le concept d'auto-adaptation, afin de décharger le concepteur au maximum de cette complexité. Mimant la plasticité cérébrale, cette architecture devient capable de s'adapter sur son environnement interne et externe de manière homéostatique. Il s'inscrit dans la famille du calcul incorporé ("embodied computing") car le substrat de calcul n'est plus pensé comme une boite noire, programmée pour une tâche donnée, mais est façonné par son environnement ainsi que par les applications qu'il supporte.Dans nos travaux, nous proposons un modèle de carte neuronale auto-organisatrice, le DMADSOM (pour Distributed Multiplicative Activity Dependent SOM), basé sur le principe des champs de neurones dynamiques (DNF pour "Dynamic Neural Fields"), pour apporter le concept de plasticité à l'architecture. Ce modèle a pour originalité de s'adapter sur les données de chaque stimulus sans besoin d'un continuum sur les stimuli consécutifs. Ce comportement généralise les cas applicatifs de ce type de réseau car l'activité est toujours calculée selon la théorie des champs neuronaux dynamique. Les réseaux DNFs ne sont pas directement portables sur les technologies matérielles d'aujourd'hui de part leurs forte connectivité. Nous proposons plusieurs solutions à ce problème. La première consiste à minimiser la connectivité et d'obtenir une approximation du comportement du réseau par apprentissage sur les connexions latérales restantes. Cela montre un bon comportement dans certain cas applicatifs. Afin de s'abstraire de ces limitations, partant du constat que lorsqu'un signal se propage de proche en proche sur une topologie en grille, le temps de propagation représente la distance parcourue, nous proposons aussi deux méthodes qui permettent d'émuler, cette fois, l'ensemble de la large connectivité des Neural Fields de manière efficace et proche des technologies matérielles. Le premier substrat calcule les potentiels transmis sur le réseau par itérations successives en laissant les données se propager dans toutes les directions. Il est capable, en un minimum d'itérations, de calculer l'ensemble des potentiels latéraux de la carte grâce à une pondération particulière de l'ensemble des itérations.Le second passe par une représentation à spikes des potentiels qui transitent sur la grille sans cycles et reconstitue l'ensemble des potentiels latéraux au fil des itérations de propagation.Le réseau supporté par ces substrats est capable de caractériser les densités statistiques des données à traiter par l'architecture et de contrôler, de manière distribuée, l'allocation des cellules de calcul. / The increasing degree of parallelism on chip which comes from the always increasing integration density, raises a number of challenges such as routing information that confronts the "bottleneck problem" or the simple difficulty to exploit massive parallelism thanks to modern computing paradigms which derived mostly from a sequential history.In order to discharge the designer of this complexity, we design a new type of bio-inspired self-adaptive architecture. Mimicking brain plasticity, this architecture is able to adapt to its internal and external environment and becomes homeostatic. Belonging to the embodied computing theory, the computing substrate is no longer thought of as a black box, programmed for a given task, but is shaped by its environment and by applications that it supports.In our work, we propose a model of self-organizing neural map, DMADSOM (for Distributed Multiplicative Activity Dependent SOM), based on the principle of dynamic neural fields (DNF for "Dynamic Neural Fields"), to bring the concept of hardware plasticity. This model is able to adapt the data of each stimulus without need of a continuum on consecutive stimuli. This behavior generalizes the case of applications of such networks. The activity remains calculated using the dynamic neural field theory. The DNFs networks are not directly portable onto hardware technology today because of their large connectivity. We propose models that bring solutions to this problem. The first is to minimize connectivity and to approximate the global behavior thanks to a learning rule on the remaining lateral connections. This shows good behavior in some application cases. In order to reach the general case, based on the observation that when a signal travels from place to place on a grid topology, the delay represents the distance, we also propose two methods to emulate the whole wide connectivity of the Neural Field with respect to hardware technology constraints. The first substrate calculates the transmitted potential over the network by iteratively allowing the data to propagate in all directions. It is capable, in a minimum of iterations, to compute the lateral potentials of the map with a particular weighting of all iterations.The second involves a spike representation of the synaptic potential and transmits them on the grid without cycles. This one is hightly customisable and allows a very low complexity while still being capable to compute the lateral potentials.The network supported, by these substrates, is capable of characterizing the statistics densities of the data to be processed by the architecture, and to control in a distributed manner the allocation of computation cells.

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