Online memory compression for embedded systems

  • Authors:

  • Lei Yang;Robert P. Dick;Haris Lekatsas;Srimat Chakradhar

  • Affiliations:

  • Northwestern University;Northwestern University;NEC Laboratories America;NEC Laboratories America

  • Venue:
  • ACM Transactions on Embedded Computing Systems (TECS)
  • Year:
  • 2010

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Abstract

Memory is a scarce resource during embedded system design. Increasing memory often increases packaging costs, cooling costs, size, and power consumption. This article presents CRAMES, a novel and efficient software-based RAM compression technique for embedded systems. The goal of CRAMES is to dramatically increase effective memory capacity without hardware or application design changes, while maintaining high performance and low energy consumption. To achieve this goal, CRAMES takes advantage of an operating system's virtual memory infrastructure by storing swapped-out pages in compressed format. It dynamically adjusts the size of the compressed RAM area, protecting applications capable of running without it from performance or energy consumption penalties. In addition to compressing working data sets, CRAMES also enables efficient in-RAM filesystem compression, thereby further increasing RAM capacity. CRAMES was implemented as a loadable module for the Linux kernel and evaluated on a battery-powered embedded system. Experimental results indicate that CRAMES is capable of doubling the amount of RAM available to applications running on the original system hardware. Execution time and energy consumption for a broad range of examples are rarely affected. When physical RAM is reduced to 62.5% of its original quantity, CRAMES enables the target embedded system to support the same applications with reasonable performance and energy consumption penalties (on average 9.5% and 10.5%), while without CRAMES those applications either may not execute or suffer from extreme performance degradation or instability. In addition to presenting a novel framework for dynamic data memory compression and in-RAM filesystem compression in embedded systems, this work identifies the software-based compression algorithms that are most appropriate for use in low-power embedded systems.