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Preparation of Fractal-Inspired Computational Architectures for Advanced Large Language Model Analysis

Yash Mittal, Dmitry Ignatov, Radu Timofte

TL;DR

The paper presents FractalNet, a fractal-inspired, template-driven pipeline that automatically generates and evaluates over a thousand convolutional architectures. By using recursive, self-similar multi-column designs and a three-component workflow (Generator, Template, Runner), it enables large-scale architectural exploration with Graphical Processing Unit (GPU) efficiency via AMP and gradient checkpointing. Evaluations on CIFAR-10 across five epochs show competitive mean performance and robust convergence, highlighting improved learning dynamics over a plain CNN baseline and favorable trade-offs versus NAS methods. The work argues for fractal-based automated architecture search as a feasible, scalable approach for resource-constrained automated design and lays groundwork for expansion to larger datasets and multimodal tasks within the LEMUR ecosystem.

Abstract

It introduces FractalNet, a fractal-inspired computational architectures for advanced large language model analysis that mainly challenges model diversity on a large scale in an efficient manner. The new set-up involves a template-driven generator, runner, and evaluation framework that, through systematic permutations of convolutional, normalization, activation, and dropout layers, can create more than 1,200 variants of neural networks. Fractal templates allow for structural recursion and multi-column pathways, thus, models become deeper and wider in a balanced way. Training utilizes PyTorch, Automatic Mixed Precision (AMP), and gradient checkpointing and is carried out on the CIFAR-10 dataset for five epochs. The outcomes show that fractal-based architectures are capable of strong performance and are computationally efficient. The paper positions fractal design as a feasible and resource-efficient method of automated architecture exploration.

Preparation of Fractal-Inspired Computational Architectures for Advanced Large Language Model Analysis

TL;DR

The paper presents FractalNet, a fractal-inspired, template-driven pipeline that automatically generates and evaluates over a thousand convolutional architectures. By using recursive, self-similar multi-column designs and a three-component workflow (Generator, Template, Runner), it enables large-scale architectural exploration with Graphical Processing Unit (GPU) efficiency via AMP and gradient checkpointing. Evaluations on CIFAR-10 across five epochs show competitive mean performance and robust convergence, highlighting improved learning dynamics over a plain CNN baseline and favorable trade-offs versus NAS methods. The work argues for fractal-based automated architecture search as a feasible, scalable approach for resource-constrained automated design and lays groundwork for expansion to larger datasets and multimodal tasks within the LEMUR ecosystem.

Abstract

It introduces FractalNet, a fractal-inspired computational architectures for advanced large language model analysis that mainly challenges model diversity on a large scale in an efficient manner. The new set-up involves a template-driven generator, runner, and evaluation framework that, through systematic permutations of convolutional, normalization, activation, and dropout layers, can create more than 1,200 variants of neural networks. Fractal templates allow for structural recursion and multi-column pathways, thus, models become deeper and wider in a balanced way. Training utilizes PyTorch, Automatic Mixed Precision (AMP), and gradient checkpointing and is carried out on the CIFAR-10 dataset for five epochs. The outcomes show that fractal-based architectures are capable of strong performance and are computationally efficient. The paper positions fractal design as a feasible and resource-efficient method of automated architecture exploration.

Paper Structure

This paper contains 13 sections, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Training and Testing
  5. Evaluation
  6. Evaluation Protocol
  7. Observed Training Behavior
  8. Results and Discussion
  9. Overall Performance Summary
  10. Comparative Analysis
  11. Discussion
  12. Limitations
  13. Conclusion

Figures (4)

  • Figure 1: Workflow of FractalNet
  • Figure 2: Overview of the CIFAR-10 dataset, which includes ten image categories representing everyday objects and animals: Airplane, Automobile, Bird, Cat, Deer, Dog, Frog, Horse, Ship, and Truck.
  • Figure 3: Validation accuracies of all FractalNet models over the first and fifth epochs, showing both initial learning dynamics and final convergence stability.
  • Figure 4: Comparison of training loss progression across epochs between Plain Networks and FractalNet larsson2017fractalnetultradeepneuralnetworks.