Accelerating Generalized Linear Models with MLWeaving: A One-Size-Fits-All System for Any-Precision Learning
Summary: MLWeaving: compact in-memory GLM layout for low-precision data, enabling retrieval at any precision inside DB engines. FPGA-accelerated SGD with dynamic precision tuning, avoiding fixed-precision bottlenecks; up to 16× faster convergence than CPU low-precision methods. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Zeke Wang (ETH Zurich)
- 2. Kaan Kara (ETH Zurich)
- 3. Hantian Zhang (ETH Zurich)
- 4. Gustavo Alonso (ETH Zurich)
- 5. Onur Mutlu (ETH Zurich)
- 6. Ce Zhang (ETH Zurich)
BibTeX Citation
@article{wang_vldb19,
title = {{Accelerating Generalized Linear Models with MLWeaving: A One-Size-Fits-All System for Any-Precision Learning}},
author = {Wang, Zeke and Kara, Kaan and Zhang, Hantian and Alonso, Gustavo and Mutlu, Onur and Zhang, Ce},
journal = {PVLDB},
series = {{VLDB} '19},
volume = {12},
number = {7},
pages = {807--821},
doi = {10.14778/3317315.3317322},
url = {https://doi.org/10.14778/3317315.3317322},
year = {2019}
}
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