Refresh Enabled Video Analytics (REVA): Implications on power and performance of DRAM supported embedded visual systems
Refresh Enabled Video Analytics (REVA): Implications on power and performance of DRAM supported embedded visual systems
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支持刷新的视频分析 (REVA)：对 DRAM 支持的嵌入式视觉系统的功耗和性能的影响
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发表时间：
2014
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通讯作者：
N. Vijaykrishnan
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作者：
Siddharth Advani;Nandhini Chandramoorthy;Karthik Swaminathan;K. M. Irick;Yong Cheol Peter Cho;Jack Sampson;N. Vijaykrishnan
视频应用在移动的和嵌入式系统中变得无处不在。可穿戴视频系统（如Google眼镜）需要实时视频分析功能和延长电池寿命。此外，这些移动的系统中的图像传感器的分辨率的增加对存储器存储以及计算能力提出了增加的需求。在这项工作中，我们提出了刷新启用视频分析（REVA）系统，一个嵌入式架构的多对象场景的理解和解决实时嵌入式视频分析应用程序提供的独特的机会，以减少DRAM内存刷新能量。我们比较我们的设计与现有的设计空间，并显示节省88%的刷新功率和15%的总功率，相比，一个标准的DRAM刷新方案。
Video applications are becoming ubiquitous in mobile and embedded systems. Wearable video systems such as Google Glasses require capabilities for real-time video analytics and prolonged battery lifetimes. Further, the increasing resolution of image sensors in these mobile systems places an increasing demand on both the memory storage as well as the computational power. In this work, we present the Refresh Enabled Video Analytics (REVA) system, an embedded architecture for multi-object scene understanding and tackle the unique opportunities provided by real-time embedded video analytics applications for reducing the DRAM memory refresh energy. We compare our design with the existing design space and show savings of 88% in refresh power and 15% in total power, as compared to a standard DRAM refresh scheme.