Hardware/Software Architectures for Low-Power Embedded Multimedia Systems Hardware/Software Architectures for Low-Power Embedded Multimedia Systems

Hardware/Software Architectures for Low-Power Embedded Multimedia Systems

    • CHF 95.00
    • CHF 95.00

Beschreibung des Verlags

The extreme complexity/energy requirements and context-aware processing nature of multimedia applications stimulate the need for adaptive low-power embedded multimedia systems with high-performance. Run-time adaptivity is required to react to the run-time varying scenarios (e.g., quality and performance constraints, available energy, input data). 

This book presents techniques for energy reduction in adaptive embedded multimedia systems, based on dynamically reconfigurable processors.  The approach described will enable designers to meet performance/area constraints, while minimizing video quality degradation, under various, run-time scenarios.  Emphasis is placed on implementing power/energy reduction at various abstraction levels. To enable this, novel techniques for adaptive energy management at both processor architecture and application architecture levels are presented, such that both hardware and software adapt together, minimizing overall energy consumption under unpredictable, design-/compile-time scenarios.
 
Introduces general concepts and requirements of embedded multimedia systems based on advanced video codecs, dynamically reconfigurable processors, and low-power techniques in reconfigurable computing; Describes novel techniques and concepts for providing adaptivity and energy reduction jointly at processor and application architecture levels; Provides techniques for enabling run-time configurability for quality vs. energy consumption tradeoff at the application level

GENRE
Gewerbe und Technik
ERSCHIENEN
2011
25. Juli
SPRACHE
EN
Englisch
UMFANG
244
Seiten
VERLAG
Springer New York
GRÖSSE
7.2
 MB

Mehr Bücher von Muhammad Shafique & Jörg Henkel

Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing
2023
Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing
2023
Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing
2023
Approximate Circuits Approximate Circuits
2018
Advanced Techniques for Power, Energy, and Thermal Management for Clustered Manycores Advanced Techniques for Power, Energy, and Thermal Management for Clustered Manycores
2018
Energy Efficient Embedded Video Processing Systems Energy Efficient Embedded Video Processing Systems
2017