3M-AI:云环境下多fpga AI系统的多任务多核虚拟化框架

Shulin Zeng, Guohao Dai, Hanbo Sun, Jun Liu, Hongren Zheng, Yusong Wu, Fan Zhang, Xinhao Yang, Yi Cai, Yu Wang, Huazhong Yang
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引用次数: 2

摘要

多任务、动态工作负载和远程访问三个基本特性是硬件虚拟化的基础。此外,用于神经网络加速器的SOTA多DNN调度算法没有考虑多核DNN加速器的多任务并发执行和资源分配问题。此外,现有的GPU虚拟化解决方案可能会引入巨大的远程访问延迟开销,导致系统性能严重下降。为了应对这些挑战,我们提出了3M-AI,这是一种用于云中的多fpga AI系统的多任务多核虚拟化框架。3M-AI通过优化fpga之间的数据同步和移动,实现多fpga上的模型并行性。3M-AI利用启发式硬件资源分配算法和精确的多核延迟预测模型。与GPU虚拟化解决方案相比,3M-AI将远程API访问开销显著降低到近1%,并且在批处理大小为1的情况下实现了更好的NN推理延迟。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
3M-AI: A Multi-task and Multi-core Virtualization Framework for Multi-FPGA AI Systems in the Cloud
With the ever-growing demands for online Artificial Intelligence (AI), the hardware virtualization support for deep learning accelerators is vital for providing AI capability in the cloud. Three basic features, multi-task, dynamic workload, and remote access, are fundamental for hardware virtualization. However, most of the deep learning accelerators do not support concurrent execution of multiple tasks. Besides, the SOTA multi-DNN scheduling algorithm for NN accelerators neither consider the multi-task concurrent execution and resources allocation for the multi-core DNN accelerators. Moreover, existing GPU virtualized solutions could introduce a huge remote access latency overhead, resulting in a severe system performance drop. In order to tackle these challenges, we propose 3M-AI, a Multi-task and Multi-core virtualization framework for Multi-FPGA AI systems in the cloud. 3M-AI enables model parallelism on multi-FPGA by optimizing data synchronization and movement between FPGAs. 3M-AI exploits heuristic hardware resource allocation algorithm and accurate multi-core latency prediction model. 3M-AI significantly reduces the remote API access overhead to nearly 1%, and achieves better NN inference latency with a batch size 1 compared with GPU virtualization solutions.
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