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
Sustainability Initiatives and Cloud Computing: Reducing Carbon Footprint in Data Centers (Published)
Cloud computing has emerged as a transformative solution to address the environmental challenges posed by traditional data centers. As digital infrastructure continues to expand, the technology sector faces increasing scrutiny regarding its carbon footprint. This article explores the intersection of sustainability initiatives and cloud computing, highlighting how innovative approaches are reducing environmental impact while maintaining performance and reliability. Cloud platforms achieve significant benefits through resource optimization, energy efficiency investments, and renewable energy integration. Technological innovations driving sustainability include advanced cooling technologies, workload optimization strategies, and hardware improvements. Organizations can implement hybrid and multi-cloud approaches, cloud-native application designs, and comprehensive sustainability metrics to further enhance environmental performance. Emerging trends such as circular economy principles, edge computing optimization, and quantum computing integration promise additional sustainability advances in the future.
Keywords: Cloud Migration, carbon footprint, circular economy, edge computing, virtualization