Younger: The First Dataset for Artificial Intelligence-Generated Neural Network Architecture

TL;DR

Younger is a comprehensive dataset of over 7,600 neural network architectures designed to facilitate automated architecture generation and serve as a benchmark for graph neural network research.

Contribution

The paper introduces Younger, the first large-scale dataset of neural architectures, enabling new methods for automated design and benchmarking in AI-generated neural network architecture.

Findings

Younger effectively supports automated architecture generation.

The dataset serves as a benchmark for graph neural network development.

Experiments demonstrate the dataset's utility in architecture synthesis.

Abstract

Designing and optimizing neural network architectures typically requires extensive expertise, starting with handcrafted designs and then manual or automated refinement. This dependency presents a significant barrier to rapid innovation. Recognizing the complexity of automatically generating neural network architecture from scratch, we introduce Younger, a pioneering dataset to advance this ambitious goal. Derived from over 174K real-world models across more than 30 tasks from various public model hubs, Younger includes 7,629 unique architectures, and each is represented as a directed acyclic graph with detailed operator-level information. The dataset facilitates two primary design paradigms: global, for creating complete architectures from scratch, and local, for detailed architecture component refinement. By establishing these capabilities, Younger contributes to a new frontier, Artificial Intelligence-Generated Neural Network Architecture (AIGNNA). Our experiments explore the potential and effectiveness of Younger for automated architecture generation and, as a secondary benefit, demonstrate that Younger can serve as a benchmark dataset, advancing the development of graph neural networks. We release the dataset and code publicly to lower the entry barriers and encourage further research in this challenging area.