Enabling Efficient and Flexible FPGA Virtualization for Deep Learning in the Cloud

Shulin Zeng
Shulin Zeng
Guohao Dai
Guohao Dai
Hanbo Sun
Hanbo Sun
Kai Zhong
Kai Zhong
Guangjun Ge
Guangjun Ge
Kaiyuan Guo
Kaiyuan Guo

FCCM, pp. 102-110, 2020.

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Abstract:

FPGAs have shown great potential in providing low-latency and energy-efficient solutions for deep neural network (DNN) inference applications. Currently, the majority of FPGA-based DNN accelerators in the cloud run in a time-division multiplexing way for multiple users sharing a single FPGA, and require re-compilation with $\\sim$100s ove...More

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