Image bitmaps, i.e., data containing pixels and visual perception, have been widely used in emerging applications for pixel operations while consuming lots of memory space and energy. Compared with legacy DRAM, non-volatile memories (NVMs) are suitable for bitmap storage due to the salient features of high density and intrinsic durability. However, writing NVMs suffers from higher energy consumption and latency compared with read accesses. Existing precise or approximate compression schemes in NVM controllers show limited performance for bitmaps due to the irregular data patterns and variance in bitmaps. We observe the pixel-level similarity when writing bitmaps due to the analogous contents in adjacent pixels. By exploiting the pixel-level similarity, we propose SimCom, an approximate similarity-aware compression scheme in the NVM module controller, to efficiently compress data for each write access on-the-fly. The idea behind SimCom is to compress continuous similar words into the pairs of base words with runs. The storage costs for small runs are further mitigated by reusing the least significant bits of base words. SimCom adaptively selects an appropriate compression mode for various bitmap formats, thus achieving an efficient trade-off between quality and memory performance. We implement SimCom on GEM5/zsim with NVMain and evaluate the performance with real-world image/video workloads. Our results demonstrate the efficacy and efficiency of our SimCom with an efficient quality-performance trade-off.

中文摘要：

1、研究背景（context）：
图像位图，即包含像素等具体视觉信息的数据，被广泛应用在图像/视频处理、计算机视觉、机器学习和人工智能等新兴应用。内存中的位图数据体积较大，消耗了大量的能源。和传统的DRAM相比，非易失内存（non-volatile memory，NVM）具有高存储密度和免动态刷新的优势，因此适合基于图像的应用程序。然而，非易失内存的写操作的延迟和功耗较高。在非易失内存控制器上难以实现传统图像编码算法。现有的面向内存控制器的压缩方法对于位图中的不规则数据压缩效果有限，位图很难匹配通用压缩方法中的数据模式。
2、目的（Objective）：
提高面向非易失内存的位图数据存储性能，降低写延迟和能耗；同时适配不同的位图格式，以支持实际应用中的多种位图格式共存的真实场景。
3、方法（Method）：
实验测试发现非易失内存写请求中的位图数据普遍存在像素级别的相似性，提出相似性感知的近似编码方案SimCom。基于像素级别的相似性，SimCom将连续的相似的字压缩为一个基字和次数。另外，重用基字的最低位以编码较小的次数。SimCom在并行执行的多个压缩模块中自适应地选择合适的模式，最终实现位图质量和内存性能之间的高效权衡。
4、结果（Result & Findings）：
实验在GEM5和zsim平台分别评估了图像和视频工作负载下的非易失内存系统性能。实验结果表明，在3%的精度损失情况下，SimCom相比于现有FPC/BDI/BiScaling减少35.4%/39.6%/34.4%的写比特数。SimCom在3%的精度损失下的写优化带来了比FPC/BDI/BiScaling低18.3%/22.2%/21.1%的能耗表现和低17.3%/24.9%/28.8%的写操作延迟。另外，自适应的编码能有效支持不同位图格式。
5、结论（Conclusions）：
实验结果表明，位图数据中普遍存在像素级别的相似性。SimCom设计了相似性感知的自适应近似压缩方案，实验结果表明近似压缩可以在位图质量和内存性能之间实现权衡，有效地降低了非易失内存的写操作能耗和延迟。在未来工作中，SimCom可以结合不同块之间的相似性，进一步提升非易失内存系统性能。

Key words: approximate computing; data compression; memory architecture; non-volatile memory;