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System-level exploration of association table implementations in telecom network applications

System-level exploration of association table implementations in telecom network applications 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Embedded Computing Systems (TECS) Association for Computing Machinery

System-level exploration of association table implementations in telecom network applications

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2002 by ACM Inc.
ISSN
1539-9087
DOI
10.1145/581888.581895
Publisher site
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Abstract

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.

Journal

ACM Transactions on Embedded Computing Systems (TECS) – Association for Computing Machinery

Published: Nov 1, 2002

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