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Post-Training 4-bit Quantization on Embedding Tables

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arxiv 1911.02079 v1 pith:WY6ZD563 submitted 2019-11-05 cs.LG cs.IRstat.ML

Hui Guan , Andrey Malevich , Jiyan Yang , Jongsoo Park , Hector Yuen This is my paper
classification cs.LGcs.IRstat.ML
keywords quantizationembeddingmodelcontinuousentitieslargepost-traininguniform
verification ladder T0 review T1 audit T2 compute T3 formal
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Continuous representations have been widely adopted in recommender systems where a large number of entities are represented using embedding vectors. As the cardinality of the entities increases, the embedding components can easily contain millions of parameters and become the bottleneck in both storage and inference due to large memory consumption. This work focuses on post-training 4-bit quantization on the continuous embeddings. We propose row-wise uniform quantization with greedy search and codebook-based quantization that consistently outperforms state-of-the-art quantization approaches on reducing accuracy degradation. We deploy our uniform quantization technique on a production model in Facebook and demonstrate that it can reduce the model size to only 13.89% of the single-precision version while the model quality stays neutral.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CoVE: Compressed Vocabulary Expansion Makes Better LLM-based Recommender Systems

    cs.IR 2025-06 conditional novelty 6.0 of 10

    CoVE assigns each item a unique token ID, tunes item embeddings and the LM head, and predicts the next item from logits, beating finetune-and-retrieval baselines by up to 62 percent with a 16x compressed embedding table.

  2. HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption

    cs.CR 2025-06 conditional novelty 6.0 of 10

    Client-side digit decomposition and block-diagonal packing enable server-side private embedding lookups in FHE, cutting embedding-lookup latency by up to 56x versus CodedHeLUT and enabling end-to-end encrypted DLRM in...

  3. Rec-AD: An Efficient Computation Framework for FDIA Detection Based on Tensor Train Decomposition and Deep Learning Recommendation Model

    cs.LG 2025-07 conditional novelty 5.0 of 10

    Rec-AD applies tensor-train embedding compression plus index reordering and pipeline training to DLRM-style FDIA detection, reporting 5-74x smaller embedding tables and up to 3x faster training at nearly unchanged accuracy.

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