19 citations · 39 across the 5 of their papers we have counts for
5 papers
Revisiting Neural Retrieval on Accelerators
Jiaqi Zhai, Zhaojie Gong, Yueming Wang +4
Retrieval finds a small number of relevant candidates from a large corpus for information retrieval and recommendation applications. A key component of retrieval is to model (user,…
MTrainS: Improving DLRM training efficiency using heterogeneous memories
Hiwot Tadese Kassa, Paul Johnson, Jason Akers +7
Recommendation models are very large, requiring terabytes (TB) of memory during training. In pursuit of better quality, the model size and complexity grow over time, which requires…
Cross-Region Domain Adaptation for Class-level Alignment
Zhijie Wang, Xing Liu, Masanori Suganuma +1
Semantic segmentation requires a lot of training data, which necessitates costly annotation. There have been many studies on unsupervised domain adaptation (UDA) from one domain to…
Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models
Dheevatsa Mudigere, Yuchen Hao, Jianyu Huang +50
Deep learning recommendation models (DLRMs) are used across many business-critical services at Facebook and are the single largest AI application in terms of infrastructure demand…
TT-Rec: Tensor Train Compression for Deep Learning Recommendation Models
Chunxing Yin, Bilge Acun, Xing Liu +1
The memory capacity of embedding tables in deep learning recommendation models (DLRMs) is increasing dramatically from tens of GBs to TBs across the industry. Given the fast growth…