Exploiting On-Device Image Classification for Energy Efficiency in Ambient-Aware Systems [chapter]

Mohammed Shoaib, Swagath Venkataramani, Xian-Sheng Hua, Jie Liu, Jin Li
2015 Mobile Cloud Visual Media Computing  
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 naïve 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
more » ... e 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 hierarchicallypipelined 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.
doi:10.1007/978-3-319-24702-1_7 fatcat:oftquf42cbftddqll5znh3jf64