Exploiting On-Device Image Classification for Energy Efficiency in Ambient-Aware Systems
Exploiting On-Device Image Classification for Energy Efficiency in Ambient-Aware Systems
复制标题
利用设备上的图像分类来提高环境感知系统的能源效率
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Jin Li
中科院分区：
文献类型：
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作者：
M. Shoaib;Swagath Venkataramani;Xiansheng Hua;Jie Liu;Jin Li
环境感知应用程序需要知道环境中有哪些对象。虽然视频数据包含这些信息，但分析它是一个挑战，特别是在能量和存储受限的便携式设备上。一个简单的解决方案是将视频采样并传输到云端，在那里可以使用高级算法进行分析。然而，这增加了通信能量成本，使得这种方法不切实际。在这篇文章中，我们将展示如何通过简单的设备上计算来降低此类系统的能量。特别是，我们使用一个低复杂度的基于特征的图像分类器来过滤掉视频中不必要的帧。为了降低处理能量和维持高吞吐量，我们提出了一个分层流水线的硬件架构的图像分类器。基于ASIC在45 nm SOI工艺中的合成结果，我们证明了分类器可以在12 fps的帧速率下实现最小能量操作，而每帧仅消耗3 mJ的能量。使用一个原型系统，我们估计约70%的通信能量减少时，5%的帧是有趣的视频流。
Ambient-aware applications need to know what objects are in the environment. Although video data contains this information, analyzing it is a challenge esp. on portable devices that are constrained in energy and storage. A naive solution is to sample and stream video to the cloud, where advanced algorithms can be used for analysis. However, this increases communication energy costs, making this approach impractical. In this article, we show how to reduce energy in such systems by employing simple on-device computations. In particular, we use a low-complexity feature-based image classifier to filter out unnecessary frames from video. To lower the processing energy and sustain a high throughput, we propose a hierarchically pipelined hardware architecture for the image classifier. Based on synthesis results from an ASIC in a 45 nm SOI process, we demonstrate that the classifier can achieve minimum-energy operation at a frame rate of 12 fps, while consuming only 3 mJ of energy per frame. Using a prototype system, we estimate about 70 % reduction in communication energy when 5 % of frames are interesting in a video stream.