Calcul non conventionnel avec des nanocomposants memristifs : du calcul numérique aux accélérateurs neuromorphiques

By 2020, there will be 50 to 100 billion devices connected to the Internet. Two domains of hot researchto address these high demands of data processing are the Internet of Things (IoT) and Big Data. Thedemands of these new applications are increasing faster than the development of new hardwareparticularly because of the slowdown of Moore’s law. The main reason of the ineffectiveness ofthe processing speed is the memory wall or Von Neumann bottleneck which is comming from speeddifferences between the processor and the memory. Therefore, a new fast and power-efficient hardwarearchitecture is needed to respond to those huge demands of data processing.In this thesis, we introduce novel high performance architectures for next generation computingusing emerging nanotechnologies such as memristors. We have studied unconventional computingmethods both in the digital and the analog domains. However, the main focus and contribution is inSpiking Neural Network (SNN) or neuromorphic analog computing. In the first part of this dissertation,we review the memristive devices proposed in the literature and study their applicability in a hardwarecrossbar digital architecture. At the end of part I, we review the Neuromorphic and SNN architecture.The second part of the thesis contains the main contribution which is the development of a NeuralNetwork Scalable Spiking Simulator (N2S3) suitable for the hardware implementation of neuromorphiccomputation, the introduction of a novel synapse box which aims at better learning in SNN platforms,a parameter exploration to improve performance of memristor-based SNN, and finally a study of theapplication of deep learning in SNN.