Data-Reuse and Parallel Embedded Architectures for Low-Power, Real-Time Multimedia Applications

Exploitation of data re-use in combination with the use of custom memory hierarchy that exploits the temporal locality of data accesses may introduce significant power savings, especially for data-intensive applications. The effect of the data-reuse decisions on the power dissipation but also on area and performance of multimedia applications realized on multiple embedded cores is explored. The interaction between the data-reuse decisions and the selection of a certain data-memory architecture model is also studied. As demonstrator a widely-used video processing algorithmic kernel, namely the full search motion estimation kernel, is used. Experimental results prove that improvements in both power and performance can be acquired, when the right combination of data memory architecture model and data-reuse transformation is selected

ESVD: An Integrated Energy Scalable Framework for Low-Power Video Decoding Systems

Video applications using mobile wireless devices are a challenging task due to the limited capacity of batteries. The higher complex functionality of video decoding needs high resource requirements. Thus, power efficient control has become more critical design with devices integrating complex video processing techniques. Previous works on power efficient control in video decoding systems often aim at the low complexity design and not explicitly consider the scalable impact of subfunctions in decoding process, and seldom consider the relationship with the features of compressed video date. This paper is dedicated to developing an energy-scalable video decoding (ESVD) strategy for energy-limited mobile terminals. First, ESVE can dynamically adapt the variable energy resources due to the device aware technique. Second, ESVD combines the decoder control with decoded data, through classifying the data into different partition profiles according to its characteristics. Third, it introduces utility theoretical analysis during the resource allocation process, so as to maximize the resource utilization. Finally, it adapts the energy resource as different energy budget and generates the scalable video decoding output under energy-limited systems. Experimental results demonstrate the efficiency of the proposed approach