Introducing GA-SSNN: A Method for Optimizing Stochastic Spiking Neural Networks : Scaling the Edge User Allocation Constraint Satisfaction Problem with Enhanced Energy and Time Efficiency

Abstract: As progress within Von Neumann-based computer architecture is being limited by the physical limits of transistor size, neuromorphic comuting has emerged as a promising area of research. Neuromorphic hardware tends to be substantially more power efficient by imitating the aspects of computations in networks of neurons in the brain. It features massive parallelism, colocation of processing and memory at the neurons and synapses, inherent scalability, temporally sparse event-driven computation, and stochasticity. This thesis explores the application of neuromorphic comuting, specifically Stochastic Spiking Neural Networks (SSNNs), to large-scale edge user allocation constraint satisfaction problems (CSPs). These problems are central in the era of 5G networks, augmented reality and computational offloading, yet existing solutions struggle with scalability and stability. The thesis introduces the Genetic Algorithm for Stochastic Spiking Neural Networks (GA-SSNN), an algorithm designed to optimize complex and stochastic objective functions. The GA-SSNN algorithm leverages adaptive mutation, simulation time management, constraint approximation, and specialized tournament selection to efficiently traverse the search space and achieves better performance than the current state of the art (NSGA-II). Furthermore, the thesis elaborates on designing an SSNN structure to efficiently solve a complex CSP. The outcome of this thesis represents a significant step towards applying neuromorphic computing to real-world scenarios, with the potential to greatly enhance solution speed and energy efficiency.