Due to time and resource limitations, units are rarely able to achieve and sustain fully trained proficiency in all ...

Abstract: As the computational demands driven by large model technologies continue to grow rapidly, leveraging GPU hardware to expedite parallel training processes has emerged as a commonly-used ...

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Abstract: With the increment of data sets and models sizes, distributed deep learning has been proposed to accelerate training and improve the accuracy of DNN models. The parameter server framework is ...