Device Placement Optimization with Reinforcement Learning

    Hieu Pham
    Hieu Pham
    Benoit Steiner
    Benoit Steiner
    Yuefeng Zhou
    Yuefeng Zhou
    Naveen Kumar
    Naveen Kumar

    ICML, 2017.

    Cited by: 0|Bibtex|Views49|Links
    EI

    Abstract:

    The past few years have witnessed a growth in size and computational requirements for training and inference with neural networks. Currently, a common approach to address these requirements is to use a heterogeneous distributed environment with a mixture of hardware devices such as CPUs and GPUs. Importantly, the decision of placing parts...More

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