GLAI: GreenLightningAI for Accelerated Training through Knowledge Decoupling
Researchers have introduced GreenLightningAI (GLAI), a novel architectural block designed to replace conventional Multi-Layer Perceptrons (MLPs) in neural networks. The core innovation involves decoupling structural knowledge, encoded by stable ReLU activation patterns, from quantitative knowledge carried by weights and biases. By fixing the structure once stabilized, GLAI optimizes only the quantitative components, reformulating the MLP as a combination of paths. This approach retains the universal approximation capabilities of standard MLPs while significantly enhancing training efficiency. Experimental results indicate that GLAI reduces training time by approximately 40% on average across various cases. It consistently matches or exceeds the accuracy of traditional MLPs with equivalent parameter counts and demonstrates faster convergence. As a generic block, GLAI can be integrated into diverse architectures, including supervised heads, self-supervised learning projection layers, and few-shot classifiers. The study suggests that this new design principle offers a promising direction for optimizing large-scale models like Transformers, where MLP blocks constitute a major portion of the computational footprint.
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GLAI: GreenLightningAI for Accelerated Training through Knowledge Decoupling
Researchers have introduced GreenLightningAI (GLAI), a novel architectural block designed to replace conventional Multi-Layer Perceptrons (MLPs) in neural networks. The core innovation involves decoupling structural knowledge, encoded by stable ReLU activation patterns, from quantitative knowledge carried by weights and biases. By fixing the structure once stabilized, GLAI optimizes only the quantitative components, reformulating the MLP as a combination of paths. This approach retains the universal approximation capabilities of standard MLPs while significantly enhancing training efficiency. Experimental results indicate that GLAI reduces training time by approximately 40% on average across various cases. It consistently matches or exceeds the accuracy of traditional MLPs with equivalent parameter counts and demonstrates faster convergence. As a generic block, GLAI can be integrated into diverse architectures, including supervised heads, self-supervised learning projection layers, and few-shot classifiers. The study suggests that this new design principle offers a promising direction for optimizing large-scale models like Transformers, where MLP blocks constitute a major portion of the computational footprint.
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