Custom Memory Management Methodology: Exploration of Memory Organisation for Embedded Multimedia System Design

Catthoor, Francky; Wuytack, Sven; De Greef, G.E.; Banica, Florin; Nachtergaele, Lode; Vandecappelle, Arnout

ISBN 10: 1441950613 / ISBN 13: 9781441950611

Verlag: Springer, 2010

Beschreibung

Bestandsnummer des Verkäufers ABLIING23Mar2411530296459

Bibliografische Details

• Titel: Custom Memory Management Methodology: Exploration of Memory Organisation for Embedded Multimedia System Design
• Verlag: Springer
• Erscheinungsjahr: 2010
• Sprache: Englisch
• ISBN-10: 1441950613
• ISBN-13: 9781441950611
• Einband: Softcover
• Zustand: New

Über diesen Titel

Inhaltsangabe

The main intention of this book is to give an impression of the state-of-the-art in system-level memory management (data transfer and storage) related issues for complex data-dominated real-time signal and data processing applications. The material is based on research at IMEC in this area in the period 1989- 1997. In order to deal with the stringent timing requirements and the data dominated characteristics of this domain, we have adopted a target architecture style and a systematic methodology to make the exploration and optimization of such systems feasible. Our approach is also very heavily application­ driven which is illustrated by several realistic demonstrators, partly used as red-thread examples in the book. Moreover, the book addresses only the steps above the traditional high-level synthesis (scheduling and allocation) or compilation (traditional or ILP oriented) tasks. The latter are mainly focussed on scalar or scalar stream operations and data where the internal structure of the complex data types is not exploited, in contrast to the approaches discussed here. The proposed methodologies are largely independent of the level of programmability in the data-path and controller so they are valuable for the realisation of both hardware and software systems. Our target domain consists of signal and data processing systems which deal with large amounts of data.