UNIX internals: the new frontiers
UNIX internals: the new frontiers
A very fast algorithm for RAM compression
ACM SIGOPS Operating Systems Review
Swap compression: resurrecting old ideas
Software—Practice & Experience
Code compression for low power embedded system design
Proceedings of the 37th Annual Design Automation Conference
Improving System Performance with Compressed Memory
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
A Pipeline Architecture for Encompression (Encryption + Compression) Technology
VLSID '03 Proceedings of the 16th International Conference on VLSI Design
Hardware-Assisted Data Compression for Energy Minimization in Systems with Embedded Processors
Proceedings of the conference on Design, automation and test in Europe
High performance code compression architecture for the embedded ARM/THUMB processor
Proceedings of the 1st conference on Computing frontiers
CRAMES: compressed RAM for embedded systems
CODES+ISSS '05 Proceedings of the 3rd IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis
High-performance operating system controlled memory compression
Proceedings of the 43rd annual Design Automation Conference
Adaptive main memory compression
ATEC '05 Proceedings of the annual conference on USENIX Annual Technical Conference
The case for compressed caching in virtual memory systems
ATEC '99 Proceedings of the annual conference on USENIX Annual Technical Conference
IBM memory expansion technology (MXT)
IBM Journal of Research and Development
Memory energy minimization by data compression: algorithms, architectures and implementation
IEEE Transactions on Very Large Scale Integration (VLSI) Systems - Special section on the 2002 international symposium on low-power electronics and design (ISLPED)
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Online memory compression is a technology that increases the amount of memory available to applications by dynamically compressing and decompressing their working datasets on demand. It has proven extremely useful in embedded systems with tight physical RAM constraints. The technology can be used to increase functionality, reduce size, and reduce cost, without modifying applications or hardware. This article presents a new software-based online memory compression algorithm for embedded systems. In comparison with the best algorithms used in online memory compression, our new algorithm has a competitive compression ratio but is twice as fast. In addition, we describe several practical problems encountered in developing an online memory compression infrastructure and present solutions. We present a method of adaptively managing the uncompressed and compressed memory regions during application execution. This memory management scheme adapts to the predicted memory requirements of applications. It permits efficient compression for a wide range of applications. We have evaluated our techniques on a portable embedded device and have found that the memory available to applications can be increased by 2.5× with negligible performance and power consumption penalties, and with no changes to hardware or applications. Our techniques allow existing applications to execute with less physical memory. They also allow applications with larger working datasets to execute on unchanged embedded system hardware, thereby increasing functionality.