no code implementations • 22 Mar 2024 • Yassaman Ebrahimzadeh Maboud, Muhammad Adnan, Divya Mahajan, Prashant J. Nair
Training recommendation models pose significant challenges regarding resource utilization and performance.
no code implementations • 28 Aug 2023 • Muhammad Adnan, Yassaman Ebrahimzadeh Maboud, Divya Mahajan, Prashant J. Nair
However, deep learning-based recommendation models often face challenges due to evolving user behaviour and item features, leading to covariate shifts.
no code implementations • 11 Apr 2022 • Muhammad Adnan, Yassaman Ebrahimzadeh Maboud, Divya Mahajan, Prashant J. Nair
This approach utilizes CPU main memory for non-popular embeddings and GPUs' HBM for popular embeddings.
1 code implementation • 1 Mar 2021 • Muhammad Adnan, Yassaman Ebrahimzadeh Maboud, Divya Mahajan, Prashant J. Nair
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.