This framework introduces a novel approach for designing, measuring, and optimizing AI models through a FLOPs-centric methodology, enabling scalable deep learning with reduced computational and energy overhead. By analyzing model architecture, hardware utilization, and training efficiency, the framework supports both cloud-scale and edge AI deployments. Through comprehensive profiling, dynamic scaling, and computation-aware training, the system addresses efficiency challenges across vision, NLP, and multimodal models without compromising accuracy. The environmental impact assessment component provides organizations with tools to quantify and reduce the carbon footprint of AI workloads. Key innovations include a FLOPs-first design philosophy, granular profiling capabilities, FLOPs-aware loss formulations, and integrated benchmarking metrics that unify performance and efficiency considerations, contributing to greener, more sustainable AI development practices.
Keywords: Sustainability, carbon footprint, computational efficiency, edge optimization, neural architecture
