2 papers
cs.AR2024
Heterogeneous Acceleration Pipeline for Recommendation System Training
Muhammad Adnan, Yassaman Ebrahimzadeh Maboud, Divya Mahajan +1
Recommendation models rely on deep learning networks and large embedding tables, resulting in computationally and memory-intensive processes. These models are typically trained usi…
cs.IR2024
Accelerating Recommender Model Training by Dynamically Skipping Stale Embeddings
Yassaman Ebrahimzadeh Maboud, Muhammad Adnan, Divya Mahajan +1
Training recommendation models pose significant challenges regarding resource utilization and performance. Prior research has proposed an approach that categorizes embeddings into…