Gaining efficiency in transport services by appropriate design and implementation choices
ACM Transactions on Computer Systems (TOCS)
The Asynchronous Transfer Mode: a tutorial
Computer Networks and ISDN Systems - Special issue on the ATM—asynchronous transfer mode
Instruction level power analysis and optimization of software
Journal of VLSI Signal Processing Systems - Special issue on technologies for wireless computing
DAC '98 Proceedings of the 35th annual Design Automation Conference
Matisse: A System-on-Chip Design Methodology Emphasizing Dynamic Memory Management
Journal of VLSI Signal Processing Systems - Special issue on system level design
Custom Memory Management Methodology: Exploration of Memory Organisation for Embedded Multimedia System Design
Data Structures and Algorithms
Data Structures and Algorithms
Multi-objective abstract data type refinement for mapping tables in telecom network applications
Proceedings of the 2002 workshop on Memory system performance
Transforming set data types to power optimal data structures
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Memory management for embedded network applications
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Optimizing data structures at the modeling level in embedded multimedia
Journal of Systems Architecture: the EUROMICRO Journal
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We present a new exploration and optimization method at the system level to select customized implementations for dynamic data sets, as encountered in telecom network, database, and multimedia applications. Our method fits in the context of embedded system synthesis for such applications, and enables to further raise the abstraction level of the initial specification, where dynamic data sets can be specified without low-level details. Our method is suited for hardware and software implementations. In this paper, it mainly aims at minimizing the average memory power, although it can also be driven by other cost functions such as memory size and performance. Compared with existing methods, for large dynamic data sets, it can save up to 90% of the average memory power, while still saving up to 80% of the average memory size.