STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Artificial Intelligence Research & Advances Original Research

A Five-Layer Architectural Framework for Sustainable and Scalable AI Systems

  1. Naved Ahmed Electronics and Communication Engineering Department, Al-Kabir Institute of Management & Technology
  2. Mona Quaisar Department of Electronics and Communication Engineering (ECE),Al-Kabir Institute of Management & Technology
  3. MD. Maqsood Alam Department of Electronics and Communication Engineering (ECE),Al-Kabir Institute of Management & Technology
  4. Sudhisth Kumar Department of Electronics and Communication Engineering (ECE),Al-Kabir Institute of Management & Technology

Abstract

Artificial Intelligence (AI) is not only about algorithms. AI works like a full “stack” of layers, from electricity to real-world user applications. In this paper, we explain a simple and student-friendly Five- Layer Architecture of AI: (1) Energy, (2) Chips, (3) Infrastructure, (4) Models, and (5) Applications. Each layer supports the next layer, like a cake with multiple layers. If any layer is weak, AI systems become slow, costly, or unreliable. We describe why energy availability and carbon impact matter for training and running AI models, how specialized chips like GPUs/TPUs improve performance, and how cloud data centers, networking, and storage form the infrastructure backbone. We also summarize modern model development ideas such as transformer architectures, scaling laws, efficient training, and safe deployment. Finally, we connect these technical layers to practical applications in healthcare, education, agriculture, finance, and public services. We propose a methodology that students and researchers can use to study the AI stack in any country: define indicators for each layer, collect datasets from trusted sources, normalize and compare across countries, and interpret gaps and opportunities. A small cross-country comparison is presented using common infrastructure indicators to show how differences in energy, connectivity, and compute readiness can affect AI growth.

Keywords

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