MemZip: Exploring unconventional benefits from memory compression

Memory compression has been proposed and deployed in the past to grow the capacity of a memory system and re-duce page fault rates. Compression also has secondary ben-efits: it can reduce energy and bandwidth demands. How-ever, most prior mechanisms have been designed to focus on the capacity metric and few prior works have attempted to explicitly reduce energy or bandwidth. Further, mecha-nisms that focus on the capacity metric also require com-plex logic to locate the requested data in memory. In this paper, we design a highly simple compressed memory ar-chitecture that does not target the capacity metric. Instead, it focuses on complexity, energy, bandwidth, and reliabil-ity. It relies on rank subsetting and a careful placement of compressed data and metadata to achieve these benefits. Further, the space made available via compression is used to boost other metrics – the space can be used to implement stronger error correction codes or energy-efficient data en-codings. The best performing MemZip configuration yields a 45 % performance improvement and 57 % memory energy reduction, compared to an uncompressed non-sub-ranked baseline. Another energy-optimized configuration yields a 29.8 % performance improvement and a 79 % memory en-ergy reduction, relative to the same baseline.