This paper describes how profile-driven data compression, a very effective approach to reduce memory and bus traffic in singletask 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. This work was supported in part by HP Italiana S.p.A. under grant n. 398/2000.

Off-Line Data Profiling Techniques to Enhance Memory Compression in Embedded Systems / Benini, L.; Macii, Alberto; Macii, Enrico. - 2451:(2002), pp. 314-322. [10.1007/3-540-45716-X_31]

Off-Line Data Profiling Techniques to Enhance Memory Compression in Embedded Systems

MACII, Alberto;MACII, Enrico
2002

Abstract

This paper describes how profile-driven data compression, a very effective approach to reduce memory and bus traffic in singletask 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. This work was supported in part by HP Italiana S.p.A. under grant n. 398/2000.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/1497418
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