Transcription of Rammer: Enabling Holistic Deep Learning Compiler ...
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Rammer: Enabling Holistic Deep Learning Compiler ...
This paper is included in the Proceedings of the 14th USENIX Symposium on Operating Systems Design and ImplementationNovember 4 6, 2020978-1-939133-19-9 Open access to the Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation is sponsored by USENIXR ammer: Enabling Holistic deep Learning Compiler Optimizations with rTask sLingxiao Ma, Peking University and Microsoft Research; Zhiqiang Xie, ShanghaiTech University and Microsoft Research; Zhi Yang, Peking University; Jilong Xue, Youshan Miao, Wei Cui, Wenxiang Hu, Fan Yang, Lintao Zhang, and Lidong Zhou, Microsoft : Enabling Holistic deep Learning Compiler Optimizations withrTasksLingxiao Ma Zhiqiang Xie Zhi Yang Jilong Xue Youshan Miao Wei Cui Wenxiang Hu Fan Yang Lintao Zhang Lidong Zhou Peking University ShanghaiTech University Microsoft ResearchAbstractPerforming deep Neural Network (DNN) computation onhardware accelerators efficiently is challenging.
Deep neural network (DNN) is now a widely adopted ap-proach for image classification, natural language process-ing, and many other AI tasks. Due to its importance, many computational devices, such as CPU, GPU, FPGA, and spe-cially designed DNN accelerators have been leveraged to Both authors contributed equally. perform DNN computation.
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