Accelerating Generalized Linear Models with MLWeaving: A One-Size-Fits-All System for Any-precision Learning

@article{Wang2019AcceleratingGL,
  title={Accelerating Generalized Linear Models with MLWeaving: A One-Size-Fits-All System for Any-precision Learning},
  author={Z. Wang and Kaan Kara and H. Zhang and G. Alonso and Onur Mutlu and Ce Zhang},
  journal={Proc. VLDB Endow.},
  year={2019},
  volume={12},
  pages={807-821}
}
  • Z. Wang, Kaan Kara, +3 authors Ce Zhang
  • Published 2019
  • Computer Science, Mathematics
  • Proc. VLDB Endow.
  • Learning from the data stored in a database is an important function increasingly available in relational engines. Methods using lower precision input data are of special interest given their overall higher efficiency but, in databases, these methods have a hidden cost: the quantization of the real value into a smaller number is an expensive step. To address the issue, in this paper we present MLWeaving, a data structure and hardware acceleration technique intended to speed up learning of… CONTINUE READING
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    References

    SHOWING 1-10 OF 112 REFERENCES
    TABLA: A unified template-based framework for accelerating statistical machine learning
    • 107
    • PDF
    ColumnML: Column-Store Machine Learning with On-The-Fly Data Transformation
    • 17
    • PDF
    In-RDBMS Hardware Acceleration of Advanced Analytics
    • 23
    • PDF
    Stripes: Bit-serial deep neural network computing
    • 214
    • Highly Influential
    • PDF
    FPGA-Accelerated Dense Linear Machine Learning: A Precision-Convergence Trade-Off
    • 46
    • PDF
    doppioDB: A hardware accelerated database
    • 12
    DaDianNao: A Machine-Learning Supercomputer
    • Yunji Chen, T. Luo, +8 authors O. Temam
    • Computer Science
    • 2014 47th Annual IEEE/ACM International Symposium on Microarchitecture
    • 2014
    • 884
    • PDF
    Centaur: A Framework for Hybrid CPU-FPGA Databases
    • 46
    • PDF
    Compressed Linear Algebra for Large-Scale Machine Learning
    • 29
    • PDF
    Minerva: Enabling Low-Power, Highly-Accurate Deep Neural Network Accelerators
    • 196
    • PDF