The digital video coding process imposes severe pressure on memory traffic, leading to considerable power consumption related to frequent DRAM accesses. External off-chip memory demand needs to be minimized by clever architecture/algorithm co-design, thus saving energy and extending battery lifetime during video encoding. To exploit temporal redundancies among neighboring frames, the motion estimation (ME) algorithm searches for good matching between the current block and blocks within reference frames stored in external memory. To save energy during ME, this work performs memory accesses distribution analysis of the test zone search (TZS) ME algorithm and, based on this analysis, proposes both a multi-sector scratchpad memory design and dynamic management for the TZS memory access. Our dynamic memory management, called neighbor management, reduces both static consumption—by employing sector-level power gating—and dynamic consumption—by reducing the number of accesses for ME execution. Additionally, our dynamic management was integrated with two previously proposed solutions: a hardware reference frame compressor and the Level C data reuse scheme (using a scratchpad memory). This system achieves a memory energy consumption savings of \(99.8\%\) and, when compared to the baseline solution composed of a reference frame compressor and data reuse scheme, the memory energy consumption was reduced by \(44.1\%\) at a cost of just \(0.35\%\) loss in coding efficiency, on average. When compared with related works, our system presents better memory bandwidth/energy savings and coding efficiency results.

数字视频编码过程对内存流量施加了巨大压力，导致与频繁访问 DRAM 相关的大量功耗。需要通过巧妙的架构/算法协同设计来最小化外部片外存储器需求，从而在视频编码期间节省能源并延长电池寿命。为了利用相邻帧之间的时间冗余，运动估计 (ME) 算法搜索当前块与存储在外部存储器中的参考帧内的块之间的良好匹配。为了在 ME 期间节省能源，这项工作对测试区域搜索 (TZS) ME 算法进行了内存访问分布分析，并在此分析的基础上提出了 TZS 内存访问的多扇区暂存器内存设计和动态管理。我们的动态内存管理，称为邻居管理，通过减少 ME 执行的访问次数来减少静态消耗（通过采用扇区​​级电源门控）和动态消耗。此外，我们的动态管理与之前提出的两个解决方案集成在一起：硬件参考帧压缩器和 C 级数据重用方案（使用暂存器存储器）。该系统实现了内存能耗节省\(99.8\%\)，并且与由参考帧压缩器和数据重用方案组成的基线解决方案相比，内存能耗降低了\(44.1\%\)，成本仅为\(0.35\% \)编码效率的平均损失。与相关工作相比，我们的系统呈现出更好的内存带宽/节能和编码效率结果。