Hardware-Assisted Data Compression for Energy Minimization in Systems with Embedded Processors

In this paper, we suggest hardware-assisted data compression as a tool for reducing energy consumption of core-based embedded systems. We propose a novel and efficient architecture for on-the-fly data compression and decompression whose field of operation is the cache-to-memory path. Uncompressed cache lines are compressed before they are written back to main memory, and decompressed when cache refills take place. We explore two classes of compression methods, profile-driven and differential, since they are characterized by compact HW implementations, and we compare their performance to those provided by some state-of-the-art compression methods (e.g., we have considered a few variants of the Lempel-Ziv encoder). We present experimental results about memory traffic and energy consumption in the cache-to-memory path of a core-based system running standard benchmark programs. The achieved average energy savings range from 4.2% to 35.2%, depending on the selected compression algorithm