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Found 8 papers, 3 papers with code
Accelerating Recommender Model Training by Dynamically Skipping Stale Embeddings
Training recommendation models pose significant challenges regarding resource utilization and performance.
Accelerating String-Key Learned Index Structures via Memoization-based Incremental Training
Learned indexes use machine learning models to learn the mappings between keys and their corresponding positions in key-value indexes.
Ad-Rec: Advanced Feature Interactions to Address Covariate-Shifts in Recommendation Networks
However, deep learning-based recommendation models often face challenges due to evolving user behaviour and item features, leading to covariate shifts.
FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout
Building on this dropout technique, we develop an adaptive training framework, Federated Learning using Invariant Dropout (FLuID).
Heterogeneous Acceleration Pipeline for Recommendation System Training
Hotline increases the overall training throughput to 35. 7 epochs/hour in comparison to 5. 3 epochs/hour for the Intel-optimized DLRM baseline
Accelerating Recommendation System Training by Leveraging Popular Choices
This paper leverages this asymmetrical access pattern to offer a framework, called FAE, and proposes a hot-embedding aware data layout for training recommender models.
Efficient Algorithms for Device Placement of DNN Graph Operators
However, for such settings (large models and multiple heterogeneous devices), we require automated algorithms and toolchains that can partition the ML workload across devices.
In-RDBMS Hardware Acceleration of Advanced Analytics
The data revolution is fueled by advances in machine learning, databases, and hardware design.
