MQGrad: Reinforcement Learning of Gradient Quantization in Parameter Server

22 Apr 2018Guoxin CuiJun XuWei ZengYanyan LanJiafeng GuoXueqi Cheng

One of the most significant bottleneck in training large scale machine learning models on parameter server (PS) is the communication overhead, because it needs to frequently exchange the model gradients between the workers and servers during the training iterations. Gradient quantization has been proposed as an effective approach to reducing the communication volume... (read more)

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