Result: Adaptive and flexible dictionary code compression for embedded applications

Title:
Adaptive and flexible dictionary code compression for embedded applications
Source:
CASES 2006 (International Conference on Compilers, Architecture, and Synthesis for Embedded Systems, October 22-25, 2006, Seoul, Korea, embedded systems week 2006). :113-124
Publisher Information:
New York NY: ACM Press, 2006.
Publication Year:
2006
Physical Description:
print, 21 ref 1
Original Material:
INIST-CNRS
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
KTH ICT Dept. of Electronic, Computer and Software Systems Electrum 229, 164 40 Kista, Sweden
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.19105488
Database:
PASCAL Archive

Further Information

Dictionary code compression is a technique where long instructions in the memory are replaced with shorter code words used as index in a table to look up the original instructions. We present a new view of dictionary code compression for moderately high-performance processors for embedded applications. Previous work with dictionary code compression has shown decent performance and energy savings results which we verify with our own measurement that are more thorough than previously published. We also augment previous work with a more thorough analysis on the effects of cache and line size changes. In addition, we introduce the concept of aggregated profiling to allow for two or more programs to share the same dictionary contents. Finally, we also introduce dynamic dictionaries where the dictionary contents is considered to be part of the context of a process and show that the performance overhead of reloading the dictionary contents on a context switch is negligible while on the same time we can save considerable energy with a more specialized dictionary contents.