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01/08/2023
FlexShard: Flexible Sharding for Industry-Scale Sequence Recommendation Models
Sequence-based deep learning recommendation models (DLRMs) are an emergi...
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11/09/2022
RecD: Deduplication for End-to-End Deep Learning Recommendation Model Training Infrastructure
We present RecD (Recommendation Deduplication), a suite of end-to-end in...
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09/02/2022
Future Gradient Descent for Adapting the Temporal Shifting Data Distribution in Online Recommendation Systems
One of the key challenges of learning an online recommendation model is ...
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08/12/2022
AutoShard: Automated Embedding Table Sharding for Recommender Systems
Embedding learning is an important technique in deep recommendation mode...
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10/10/2021
Frequency-aware SGD for Efficient Embedding Learning with Provable Benefits
Embedding learning has found widespread applications in recommendation s...
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05/26/2021
Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale
Tremendous success of machine learning (ML) and the unabated growth in M...
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05/04/2021
Alternate Model Growth and Pruning for Efficient Training of Recommendation Systems
Deep learning recommendation systems at scale have provided remarkable g...
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10/18/2020
Fast Distributed Training of Deep Neural Networks: Dynamic Communication Thresholding for Model and Data Parallelism
Data Parallelism (DP) and Model Parallelism (MP) are two common paradigm...
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10/16/2020
Adaptive Dense-to-Sparse Paradigm for Pruning Online Recommendation System with Non-Stationary Data
Large scale deep learning provides a tremendous opportunity to improve t...
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10/12/2017