GLAI: GreenLightningAI for Accelerated Training through Knowledge Decoupling

2025-10-01

AI Summary

Conventional MLPs jointly optimize structural knowledge (e.g., connectivity patterns) and quantitative knowledge (e.g., weight values), resulting in inefficient training. Method: This paper introduces GreenLightningAI (GLAI), the first framework to explicitly decouple these two aspects in ReLU networks: it fixes the network topology while optimizing only path-specific weights, reformulating the MLP as a composition of structure-determined, parameter-learnable paths. Contribution/Results: GLAI preserves universal approximation capability and supports diverse learning paradigms—including supervised learning, self-supervised projection, and few-shot classification. Experiments demonstrate that GLAI achieves an average 40% training speedup across multiple benchmarks, faster convergence, and matches or exceeds baseline MLP accuracy under equal parameter counts. Its modular design ensures strong compatibility and plug-and-play applicability without architectural modification.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Search and Optimization: Learning to Search
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

In this work we introduce GreenLightningAI (GLAI), a new architectural block designed as an alternative to conventional MLPs. The central idea is to separate two types of knowledge that are usually entangled during training: (i) *structural knowledge*, encoded by the stable activation patterns induced by ReLU activations; and (ii) *quantitative knowledge*, carried by the numerical weights and biases. By fixing the structure once stabilized, GLAI reformulates the MLP as a combination of paths, where only the quantitative component is optimized. This reformulation retains the universal approximation capabilities of MLPs, yet achieves a more efficient training process, reducing training time by ~40% on average across the cases examined in this study. Crucially, GLAI is not just another classifier, but a generic block that can replace MLPs wherever they are used, from supervised heads with frozen backbones to projection layers in self-supervised learning or few-shot classifiers. Across diverse experimental setups, GLAI consistently matches or exceeds the accuracy of MLPs with an equivalent number of parameters, while converging faster. Overall, GLAI establishes a new design principle that opens a direction for future integration into large-scale architectures such as Transformers, where MLP blocks dominate the computational footprint.

Problem

• Separates structural and quantitative knowledge in neural network training
• Replaces conventional MLPs with more efficient knowledge-decoupled architecture
• Reduces training time while maintaining accuracy across diverse applications

Innovation

• Separates structural and quantitative knowledge during training
• Fixes stable activation patterns to optimize only weights
• Replaces MLPs with faster training and equivalent accuracy