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arXiv · 2505.12683

DimGrow: Memory-Efficient Field-level Embedding Dimension Search

Yihong Huang·Chen Chu
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Abstract

Key feature fields need bigger embedding dimensionality, others need smaller. This demands automated dimension allocation. Existing approaches, such as pruning or Neural Architecture Search (NAS), require training a memory-intensive SuperNet that enumerates all possible dimension combinations, which is infeasible for large feature spaces. We propose DimGrow, a lightweight approach that eliminates the SuperNet requirement. Starting training model from one dimension per feature field, DimGrow can progressively expand/shrink dimensions via importance scoring. Dimensions grow only when their importance consistently exceed a threshold, ensuring memory efficiency. Experiments on three recommendation datasets verify the effectiveness of DimGrow while it reduces training memory compared to SuperNet-based methods.

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Yihong Huang, Chen Chu. 2025-05-19. DimGrow: Memory-Efficient Field-level Embedding Dimension Search. https://arxiv.org/abs/2505.12683

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