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Parameterised system design based on genetic algorithms
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Source International Conference on Hardware Software Codesign archive
Proceedings of the ninth international symposium on Hardware/software codesign table of contents
Copenhagen, Denmark
Pages: 177 - 182  
Year of Publication: 2001
ISBN:1-58113-364-2
Authors
Giuseppe Ascia  Dipartimento di Ingegneria, Informatica e delle, Telecomunicazioni, Università di Catania, V.le Andrea Doria, 6, 95125 Catania - Italy
Vincenzo Catania  Dipartimento di Ingegneria, Informatica e delle, Telecomunicazioni, Università di Catania, V.le Andrea Doria, 6, 95125 Catania - Italy
Maurizio Palesi  Dipartimento di Ingegneria, Informatica e delle, Telecomunicazioni, Università di Catania, V.le Andrea Doria, 6, 95125 Catania - Italy
Sponsors
IEEE-ComSoc : Communications Society
IFIP WG 10.5 : IFIP WG 10.5
SIGSOFT: ACM Special Interest Group on Software Engineering
SIGDA: ACM Special Interest Group on Design Automation
Publisher
ACM  New York, NY, USA
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ABSTRACT

A recent reduction in the time to market has led to the development of a new approach to IP-based design in which a highly parametric pre-designed system-on-a-chip is configured according to the application it will have to execute. The greatest problems in this area regard exploration of the range of possible system configurations in search of the optimal configuration for a given system. There are, in fact, a number of parameters involved (bus sizes, cache configurations, software algorithms, etc.), each of which has a great impact on design constraints such as area, power and performance. An exhaustive analysis of all possible configurations is thus computationally unfeasible. In this paper we propose using genetic algorithms to determine the optimal configuration for a highly parametric system. The approach is applied to the search for the optimal configuration (in terms of area, power and mean access time) of a memory hierarchy involved in a given application.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Giuseppe Ascia: colleagues
Vincenzo Catania: colleagues
Maurizio Palesi: colleagues

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