Joint Structure-Function Neural Architecture Optimization under Resource Constraints

The rapid evolution of artificial intelligence (AI) necessitates the development of efficient neural architectures that balance performance and resource constraints. This paper introduces a novel Joint Structure-Function Optimization (JSFO) framework designed to simultaneously optimize the structural components and functional parameters of neural networks. Traditional neural architecture search (NAS) methods often treat architecture and parameter optimization as disjointed processes, leading to suboptimal results. Our framework integrates discrete architecture search with continuous parameter tuning, effectively addressing the inherent interdependencies between these two facets within a unified resource constraint paradigm. We present rigorous mathematical formulations and a structured optimization algorithm that allows for the exploration of a vast search space, resulting in high-performing models suitable for deployment on resource-constrained platforms. Experimental results on benchmark datasets, including CIFAR-10 and ImageNet, demonstrate substantial improvements in the accuracy-resource trade-off compared to state-of-the-art NAS approaches. The JSFO framework not only enhances model performance but also ensures adaptability to varying operational constraints, making it a crucial advancement in practical AI applications. This research lays the groundwork for future innovations in neural architecture design and optimization, promoting more accessible and efficient deployment of AI technologies across diverse environments.