Optimizing AI Performance at Scale: A FLOPs-Centric Framework for Efficient Deep Learning (Published)
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
Demystifying Deep Learning and Neural Networks: A Technical Overview (Published)
Deep learning has revolutionized artificial intelligence by enabling machines to learn hierarchically from data with minimal human intervention. Neural networks, inspired by the human brain’s structure, form the foundation of this paradigm shift, processing information through interconnected layers of artificial neurons to extract complex patterns from data. These architectures have transformed numerous domains including computer vision, natural language processing, and specialized applications such as autonomous vehicles and drug discovery. Despite remarkable achievements, significant challenges persist in interpretability, computational requirements, and data dependencies. Solutions including interpretable AI techniques, model compression, and transfer learning are actively addressing these limitations. The evolution of neural network designs, training methodologies, and optimization approaches continues to expand the capabilities and applications of deep learning while raising important considerations about ethics, sustainability, and accessibility.
Keywords: Neural Networks, computational efficiency, deep learning architectures, gradient descent optimization, model interpretability