  Image bitmaps have been widely used in in-memory applications, which consume
lots of storage space and energy. Compared with legacy DRAM, non-volatile
memories (NVMs) are suitable for bitmap storage due to the salient features in
capacity and power savings. However, NVMs suffer from higher latency and energy
consumption in writes compared with reads. Although compressing data in write
accesses to NVMs on-the-fly reduces the bit-writes in NVMs, existing precise or
approximate compression schemes show limited performance improvements for data
of bitmaps, due to the irregular data patterns and variance in data. We observe
that the data containing bitmaps show the pixel-level similarity due to the
analogous contents in adjacent pixels. By exploiting the pixel-level
similarity, we propose SimCom, an efficient similarity-aware compression scheme
in hardware layer, to 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. With the aid of domain knowledge of images, SimCom adaptively
selects an appropriate compression mode to achieve an efficient trade-off
between image quality and memory performance. We implement SimCom on GEM5 with
NVMain and evaluate the performance with real-world workloads. Our results
demonstrate that SimCom reduces 33.0%, 34.8% write latency and saves 28.3%,
29.0% energy than state-of-the-art FPC and BDI with minor quality loss of 3%.
