<div>Neuromorphic computing has gained tremendous interest because of its ability to overcome the limitations of traditional signal processing algorithms in data intensive applications such as image recognition, video analytics, or language translation. The new computing paradigm is built with the goal of achieving high energy efficiency, comparable to biological systems.</div><div>To achieve such energy efficiency, there is a need to explore new neuro-mimetic devices, circuits, and architecture, along with new learning algorithms. To that effect, we propose two main approaches:</div><div><br></div><div>First, we explore an energy-efficient hardware implementation of a bio-plausible Spiking Neural Network (SNN). The key highlights of our proposed system for SNNs are 1) addressing connectivity issues arising from Network On Chip (NOC)-based SNNs, and 2) proposing stochastic CMOS binary SNNs using biased random number generator (BRNG). On-chip Power Line Communication (PLC) is proposed to address the connectivity issues in NOC-based SNNs. PLC can use the on-chip power lines augmented with low-overhead receiver and transmitter to communicate data between neurons that are spatially far apart. We also propose a CMOS '<i>stochastic-bit</i>' with on-chip stochastic Spike Timing Dependent Plasticity (sSTDP) based learning for memory-compressed binary SNNs. A chip was fabricated in 90 nm CMOS process to demonstrate memory-efficient reconfigurable on-chip learning using sSTDP training. </div><div><br></div><div>Second, we explored coupled oscillatory systems for distance computation and convolution operation. Recent research on nano-oscillators has shown the possibility of using coupled oscillator networks as a core computing primitive for analog/non-Boolean computations. Spin-torque oscillator (STO) can be an attractive candidate for such oscillators because it is CMOS compatible, highly integratable, scalable, and frequency/phase tunable. Based on these promising features, we propose a new coupled-oscillator based architecture for hybrid spintronic/CMOS hardware that computes multi-dimensional norm. The hybrid system composed of an array of four injection-locked STOs and a CMOS detector is experimentally demonstrated. Energy and scaling analysis shows that the proposed STO-based coupled oscillatory system has higher energy efficiency compared to the CMOS-based system, and an order of magnitude faster computation speed in distance computation for high dimensional input vectors.</div>

Le changement de croyances vise à trouver des moyens adéquats pour faire évoluer les croyances d'un agent lorsqu'il est confronté à de nouvelles informations. Dans la plupart des travaux sur la révision de croyances, l'ensemble de croyances d'un agent est composé de croyances au sujet de l'environnement (le monde) et est représenté par un ensemble de formules de la logique classique. Dans de nombreuses applications, un agent n'est pas seul dans l'environnement, mais le partage avec d'autres agents, qui ont aussi des croyances. Ainsi les croyances sur les croyances des autres agents constituent un élément d'information important pour l'agent, afin d'être en mesure de prendre les meilleures décisions et d'effectuer les meilleures actions. L'utilisation de croyances sur les croyances des autres agents est par exemple cruciale dans la théorie des jeux. Dans cette thèse, nous étudions dans un premier temps les opérateurs de contraction propositionnelle correspondant aux opérateurs de révision de Katsuno et Mendelzon. Nous étudions ensuite une connexion entre les logiques épistémiques et la théorie du changement de croyances, proche de l'approche AGM. Nous nous sommes intéressés à l'utilisation des opérateurs qui modifient les croyances des agents dans les modèles KD45n standard. Cette tâche est plus compliquée que dans le cadre AGM standard, car, dans un contexte multi-agents, les nouvelles informations peuvent prendre différentes formes. Par exemple, chaque nouvelle information peut être observée/transmise/disponible à tous les agents ou seulement à certains d’entre eux. / Belief change is about finding appropriate ways to evolve an agent's beliefs when confronted with new pieces of information. In most works on belief revision, the set of beliefs of an agent is composed of beliefs about the environment (the world) and is represented by a set of formulas of classical logic. In many applications, an agent is not alone in the environment, but sharing with other agents, which also have beliefs. Thus beliefs about the beliefs of other agents are an important piece of information for the agent in order to be able to make the best decisions and perform the best actions. The use of beliefs about the beliefs of other agents is, for exampel, crucial in game theory. In this thesis, we first study the operators of propositional contraction corresponding to the revision operators proposed by Katsuno and Mendelzon. Then, we study a connection between epistemic logics and belief change theory, close to the AGM approach. We are interested in the use of operators that modify agent beliefs in standard KD45n models. This task is more complicated than in the standard AGM framework because, in a multi-agent context, new information can take different forms. For example, each new information can be observed/transmitted/available to all agents or only some of them.