并行低复杂度运动估计的分析与实现

L. Subramanian, H. Chandrababu, P. Moorthy, M. Kannan
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引用次数: 0

摘要

提出了一种基于增强逐次消去算法的运动估计并行结构。该算法的基本思想是通过消除对所有搜索块的重复访问、密集的存储和计算来估计目标的运动,从而降低功耗。因此,采用该算法的并行架构具有更高的能源效率,因为它不像其他块匹配算法那样需要大量的计算。此外,还使用了防止冗余内存访问的体系结构。分别使用Cadence工具、NcSim和RTL编译器,采用90 nm库进行仿真和合成。低功率,多VT和DFT流程已经执行。逻辑等价性也得到了检验
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis and Implementation of Parallel Low-Complexity Motion Estimation
This paper proposes a parallel architecture for motion estimation using the enhanced successive elimination (Enhanced SE) algorithm. The basic idea of this algorithm is to estimate the motion of an object by eliminating repeated access of search blocks, intense storage and computation, for all search blocks, thereby reducing power. Therefore, a parallel architecture employing this algorithm is more energy efficient as it is not as computation intensive as other block matching algorithms. Further, an architecture is used that prevents redundant memory accesses. The simulation and synthesis were carried out using Cadence tools, NcSim and RTL Compiler respectively, with 90 nm libraries. Low power, Multi VT and DFT flows have been executed. Logic equivalence has also been checked
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