Programming with sets; an introduction to SETL
Programming with sets; an introduction to SETL
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
Data structures, algorithms, and performance
Data structures, algorithms, and performance
Instruction level power analysis and optimization of software
Journal of VLSI Signal Processing Systems - Special issue on technologies for wireless computing
Matisse: A System-on-Chip Design Methodology Emphasizing Dynamic Memory Management
Journal of VLSI Signal Processing Systems - Special issue on system level design
C++ Standard Template Library
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Data Structures and Algorithms
Data Structures and Algorithms
Exploration and Synthesis of Dynamic Data Sets in Telecom Network Applications
Proceedings of the 12th international symposium on System synthesis
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
System-level exploration of association table implementations in telecom network applications
ACM Transactions on Embedded Computing Systems (TECS)
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We present a new multi-objective exploration method at the system level to select customized implementations for mapping tables, dynamically allocated, as encountered in telecom network, database, and multimedia applications. Our method fits in the context of embedded system synthesis for such applications, and it enables the optimization of the system-level memory management of these applications. To this end it mainly aims at trading off the average memory footprint, number of memory accesses, and memory power. Compared with existing methods, for large mapping tables, 90% (resp. 80%) of the average memory footprint (resp. power) can be saved, without decreasing the performance.