3 citations · 5 across the 5 of their papers we have counts for
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cs.LG2023
CAFE: Towards Compact, Adaptive, and Fast Embedding for Large-scale Recommendation Models
Hailin Zhang, Zirui Liu, Boxuan Chen +4
Recently, the growing memory demands of embedding tables in Deep Learning Recommendation Models (DLRMs) pose great challenges for model training and deployment. Existing embedding…
cs.DC2023★ 3 cited
SpotServe: Serving Generative Large Language Models on Preemptible Instances
Xupeng Miao, Chunan Shi, Jiangfei Duan +4
The high computational and memory requirements of generative large language models (LLMs) make it challenging to serve them cheaply. This paper aims to reduce the monetary cost for…
cs.LG2023
Experimental Analysis of Large-scale Learnable Vector Storage Compression
Hailin Zhang, Penghao Zhao, Xupeng Miao +4
Learnable embedding vector is one of the most important applications in machine learning, and is widely used in various database-related domains. However, the high dimensionality o…