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Found 3 papers, 1 papers with code
Accelerating Communication in Deep Learning Recommendation Model Training with Dual-Level Adaptive Lossy Compression
To mitigate this, we introduce a method that employs error-bounded lossy compression to reduce the communication data size and accelerate DLRM training.
Nimble GNN Embedding with Tensor-Train Decomposition
This paper describes a new method for representing embedding tables of graph neural networks (GNNs) more compactly via tensor-train (TT) decomposition.
TT-Rec: Tensor Train Compression for Deep Learning Recommendation Models
TT-Rec achieves 117 times and 112 times model size compression, for Kaggle and Terabyte, respectively.
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