Practical dictionary management for hardware data compression
Communications of the ACM
Stratified random sampling for power estimation
Proceedings of the 1996 IEEE/ACM international conference on Computer-aided design
An object code compression approach to embedded processors
ISLPED '97 Proceedings of the 1997 international symposium on Low power electronics and design
Selective instruction compression for memory energy reduction in embedded systems
ISLPED '99 Proceedings of the 1999 international symposium on Low power electronics and design
Code compression for low power embedded system design
Proceedings of the 37th Annual Design Automation Conference
Addressing the system-on-a-chip interconnect woes through communication-based design
Proceedings of the 38th annual Design Automation Conference
Stream synthesis for efficient power simulation based on spectral transforms
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Cached-code compression for energy minimization in embedded processors
ISLPED '01 Proceedings of the 2001 international symposium on Low power electronics and design
Design and Evaluation of a Selective Compressed Memory System
ICCD '99 Proceedings of the 1999 IEEE International Conference on Computer Design
Hardware-Assisted Data Compression for Energy Minimization in Systems with Embedded Processors
Proceedings of the conference on Design, automation and test in Europe
Performance of Hardware Compressed Main Memory
HPCA '01 Proceedings of the 7th International Symposium on High-Performance Computer Architecture
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This paper describes how profile-driven data compression, a very effective approach to reduce memory and bus traffic in single-task embedded systems, can be extended to the case of systems offering multi-function services.Application-specific profiling is replaced by static data characterization, which allows to cover a larger spectrum of the system's input space; characterization is performed by either averaging several profiling runs over different application mixes, or by resorting to statistical techniques. Results concerning memory traffic show reductions ranging from 10% to 22%, depending on the adopted data characterization technique.