Hybrid Embedding Framework for Memory-Efficient Recommendation Systems

This study introduces a memory-efficient mixed representation for deep learning recommendation models (DLRM), addressing the embedding table memory bottleneck from growing data scale. By distinguishing between frequently accessed (hot) and infrequently accessed (cold) embeddings, we store hot embeddings in a compact table while representing cold embeddings using a deep hash embedding (DHE) network, significantly reducing memory usage. This hybrid approach performs table lookups for hot embeddings and parallelized computations for cold embeddings, minimizing training time while maintaining accuracy. Experimental results demonstrate that our method outperforms other embedding reduction techniques in memory efficiency, accuracy, and training speed in CPU-GPU hybrid environments.