Kosuke Nishihara

Parallelization of H.264 video decoder for embedded multicore processor

This paper presents two parallelization methods for H.264 video decoder software on an embedded multicore processor. In parallelizing the H.264 video decoder software on a typical embedded multicore processor with shared memory, there are two problems. One problem is the computational load imbalance among cores. The other problem is memory access contention. The first method is coarse, flexible partitioning adapted for the H.264 decoding functions, which can balance the load with only a small number of synchronizations. The second method is preloading based on predicting the execution time, which can reduce memory access contention and the redundant waiting time for other cores. Experimental results demonstrate that our proposed two methods can achieve significant improvement of the consumed cycles for decoding H.264 bitstreams of QVGA size from 217 to 96 Mcps (2.3 times speedup) as compared to a non-parallel decoder.

Power Saving in Mobile Devices Using Context-Aware Resource Control

We present an effective power reduction scheme for recent mobile devices, e.g., Android devices, which tend to have problems with battery life because some of their applications may be running continuous sensor operations. We propose a context-aware method to determine the minimum set of resources (processor cores and peripherals) that results in meeting a given level of performance. With it, unnecessary processor cores and peripherals can be switched-off without degrading overall performance. Our experimental results indicate that its use can result in a 45% reduction in total power consumption. Since our method does not require applications to be modified, it can even be used easily with downloaded applications.