We present a complementary system, AdaEmbed, to reduce the size of embeddings needed for the same DLRM accuracy via in-training embedding pruning.

Jul 10, 2023 � While increasing the num- ber of embedding rows typically improves model accuracy by considering more feature instances, it can lead to larger.

Adaptive embedding system for large-scale recommendation models, reducing embedding size while maintaining accuracy. Improves deployment efficiency and�...

Mar 26, 2024 � AdaEmbed: Adaptive Embedding for Large-Scale Recommendation Models. In 17th USENIX Symposium on Operating Systems Design and Implementation�...

AdaEmbed: Adaptive Embedding for Large-Scale Recommendation Models [PDF] [BibTex] Fan Lai, Wei Zhang, Rui Liu, William Tsai, Xiaohan Wei, Yuxi Hu, Sabin�...

AdaEmbed [34] is an adaptive method that identifies and records important features. ... Adaptive Embedding for Large-Scale Recommendation Models. In 17th USENIX�...

Recently, the growing memory demands of embedding tables in Deep Learning Recommendation Models (DLRMs) pose great challenges for model training and deployment.

Missing: AdaEmbed: | Show results with:AdaEmbed:

Oct 3, 2024 � Extensive experiments on three public datasets demonstrate that MPE significantly outperforms existing embedding compression methods. Remarkably�...

, Bita Rouhani et al., ISCA industry track, 2023. AdaEmbed: Adaptive Embedding for Large-Scale Recommendation Models, Fan Lai et al., OSDI, 2023. MTrainS�...