Journal of Computer Science and Technology


Approximate Similarity-Aware Compression for Non-Volatile Main Memory

Zhang-Yu Chen (陈章玉), Yu Hua (华宇), Distinguished Member, CCF, Senior Member, ACM, IEEE, Peng-Fei Zuo (左鹏飞), Yuan-Yuan Sun (孙园园), and Yun-Cheng Guo (郭云程)   

  1. Wuhan National Laboratory for Optoelectronics, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
  • Received:2022-06-14 Revised:2023-02-08 Accepted:2023-02-12
  • Contact: Yu Hua
  • About author:Yu Hua received his B.S. and Ph.D. degrees in computer science from Wuhan University, Wuhan, in 2001 and 2005, respectively. He is currently a professor at Huazhong University of Science and Technology, Wuhan. His research interests include cloud storage systems, file systems, non-volatile memory architectures, etc. He is a distinguished member of CCF and a senior member of ACM and IEEE.

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.


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。
4、结果(Result & Findings):

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

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