Reducing Bit Writes in Non-volatile Main Memory by Similarity-aware Compression

Various applications use image bitmaps (data containing pixels) in main memory for fast accesses, thereby leading to lots of memory consumption. Unlike legacy DRAM, nonvolatile memories (NVMs) have larger capacity. However, NVM writes consume higher energy and latency compared with reads. Existing data compression schemes leverage precise general-purpose data patterns or precision scaling to reduce data sizes, which suffer from limited compression performance for bitmaps due to large variance or serious quality loss. By exploiting the pixel-level similarity due to the analogous contents in adjacent pixels, we propose SimCom, an approximate Similarity-aware Compression scheme, to compress the write accesses to bitmaps in NVMs, thus efficiently improving the memory performance for image/video applications. SimCom reduces the data size by compressing data into base words and runs. The storage costs for small runs are further mitigated by reusing the least significant bits of base words. The adaptive compression scheme handles various data formats without user annotations on data types. Our experimental results with real-world image/video workloads demonstrate the efficacy and efficiency of SimCom.