SCYLLA: QoE-aware Continuous Mobile Vision with FPGA-based Dynamic Deep Neural Network Reconfiguration
SCYLLA: QoE-aware Continuous Mobile Vision with FPGA-based Dynamic Deep Neural Network Reconfiguration
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DOI：
10.1109/infocom41043.2020.9155435
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Shuang Jiang;Zhiyao Ma;Xiao Zeng;Chenren Xu;Mi Zhang;Chen Zhang;Yunxin Liu
中科院分区：
文献类型：
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作者：
Shuang Jiang;Zhiyao Ma;Xiao Zeng;Chenren Xu;Mi Zhang;Chen Zhang;Yunxin Liu
连续的移动的视觉变得越来越重要，因为它发现了引人注目的应用程序，大大改善了我们的日常生活。然而，同时满足体验质量（QoE）多样性、能源效率和多租户的要求是一项重大挑战。在本文中，我们提出了SCYLLA，一个基于FPGA的框架，使QoE感知的连续移动的视觉与动态重新配置，以有效地应对这一挑战。SCYLLA预生成FPGA设计和DNN模型池，并动态应用最佳软硬件配置，以实现并发任务QoE的最大整体性能。我们在最先进的FPGA平台上实现了SCYLLA，并在三个数据集上使用基于无人机的交通监控应用程序对SCYLLA进行了评估。我们的评估表明，SCYLLA提供了更好的设计灵活性，并实现了上级的QoE权衡比现状的基于CPU的解决方案，现有的连续移动的视觉应用程序的基础上。
Continuous mobile vision is becoming increasingly important as it finds compelling applications which substantially improve our everyday life. However, meeting the requirements of quality of experience (QoE) diversity, energy efficiency and multi-tenancy simultaneously represents a significant challenge. In this paper, we present SCYLLA, an FPGA-based framework that enables QoE-aware continuous mobile vision with dynamic reconfiguration to effectively address this challenge. SCYLLA pre-generates a pool of FPGA design and DNN models, and dynamically applies the optimal software-hardware configuration to achieve the maximum overall performance on QoE for concurrent tasks. We implement SCYLLA on state-of-the-art FPGA platform and evaluate SCYLLA using drone-based traffic surveillance application on three datasets. Our evaluation shows that SCYLLA provides much better design flexibility and achieves superior QoE trade-offs than status-quo CPU-based solution that existing continuous mobile vision applications are built upon.