Article

Free access

Representation of function variants for embedded system optimization and synthesis

Authors:

J. TeichAuthors Info & Claims

DAC '99: Proceedings of the 36th annual ACM/IEEE Design Automation Conference

Pages 517 - 522

https://doi.org/10.1145/309847.309990

Published: 01 June 1999 Publication History

PDF eReader

References

[1]

P. Chou and G. Boriello. An analysis-based approach to composition of distributed embedded systems. In Proceedings Codes/CASHE '98, pages 3-7, Seattle, USA, March 1998.

Digital Library

Google Scholar

[2]

R. Ernst. System architectures. Talk at NATO ASI on System Level Synthesis, August 1998.

Google Scholar

[3]

R. Ernst, K. Henriss, and P. Rueffer. Software signal processing on image-engine plattforms. Technical report, TU Braunschweig, August 1998.

Google Scholar

[4]

A. Jerraya et al. Hardware/Software Co-Design: Principles and Practice, chapter Languages for System-Level Specification and Design. Kluwer Academic Publishers, Boston, USA, October 1997.

Google Scholar

[5]

A. Kavalade and P. Subrahmanyam. Hardware/software partitioning for multi-function systems. In Proceedings ICCAD '97, pages 516-521, San Jose, USA, November 1997.

Digital Library

Google Scholar

[6]

K. Kim, R. Karri, and M. Potkonjak. Synthesis of application specific programmable processors. In Proceedings DAC '97, pages 353-358, Anaheim, USA, June 1997.

Digital Library

Google Scholar

[7]

Philips Semiconductors. TriMedia Processor. http://www.semiconductors.philips.com/trimedia/.

Google Scholar

[8]

D. Ziegenbein, R. Ernst, K. Richter, J. Teich, and L. Thiele. Combining multiple models of computation for scheduling and allocation. In Proceedings Codes/CASHE '98, pages 9-13, Seattle, USA, March 1998.

Digital Library

Google Scholar

[9]

D. Ziegenbein, K. Richter, R. Ernst, J. Teich, and L. Thiele. Representation of process mode correlation for scheduling. In Proceedings ICCAD '98, San Jose, USA, November 1998.

Digital Library

Google Scholar

Cited By

View all

Lin LErnst M(2004)Improving the adaptability of multi-mode systems via program steeringACM SIGSOFT Software Engineering Notes10.1145/1013886.100754029:4(206-216)Online publication date: 1-Jul-2004
https://dl.acm.org/doi/10.1145/1013886.1007540
Lin LErnst MAvrunin GRothermel G(2004)Improving the adaptability of multi-mode systems via program steeringProceedings of the 2004 ACM SIGSOFT international symposium on Software testing and analysis10.1145/1007512.1007540(206-216)Online publication date: 11-Jul-2004
https://dl.acm.org/doi/10.1145/1007512.1007540
Haubelt CTeich JRichter KErnst R(2002)Flexibility/cost-tradeoffs of platform-based systemsEmbedded processor design challenges10.5555/765198.765202(38-56)Online publication date: 1-Jan-2002
https://dl.acm.org/doi/10.5555/765198.765202
Show More Cited By

Index Terms

Representation of function variants for embedded system optimization and synthesis
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Embedded systems
2. Software and its engineering
  1. Software organization and properties
    1. Software system structures
      1. Embedded software
      2. Real-time systems software

Recommendations

Chaotic dynamic weight particle swarm optimization for numerical function optimization

Particle swarm optimization (PSO), which is inspired by social behaviors of individuals in bird swarms, is a nature-inspired and global optimization algorithm. The PSO method is easy to implement and has shown good performance for many real-world ...
Function Call Optimization in Behavioral Synthesis
DSD '06: Proceedings of the 9th EUROMICRO Conference on Digital System Design

Behavioral synthesis, which automatically synthesizes an RTL circuit from a sequential program, is one of promising technologies to improve the design productivity. However, behavioral synthesis has not become popular yet in industry since the quality ...
Development and investigation of efficient artificial bee colony algorithm for numerical function optimization

Artificial bee colony algorithm (ABC), which is inspired by the foraging behavior of honey bee swarm, is a biological-inspired optimization. It shows more effective than genetic algorithm (GA), particle swarm optimization (PSO) and ant colony ...

Comments

Information & Contributors

Information

Published In

DAC '99: Proceedings of the 36th annual ACM/IEEE Design Automation Conference

June 1999

1000 pages

ISBN:1581131097

DOI:10.1145/309847

Editor:
Mary Jane Irwin

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 June 1999

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Article

Conference

DAC99

Sponsor:

EDAC
SIGDA
IEEE-CAS

DAC99: The 36th ACM/IEEE-CAS/EDAC Design Automation Conference

June 21 - 25, 1999

Louisiana, New Orleans, USA

Acceptance Rates

DAC '99 Paper Acceptance Rate 154 of 451 submissions, 34%;

Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

Upcoming Conference

DAC '25

Sponsor:
sigda

62nd ACM/IEEE Design Automation Conference

June 22 - 26, 2025

San Francisco , CA , USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
166
Total Downloads

Downloads (Last 12 months)9
Downloads (Last 6 weeks)1

Reflects downloads up to 21 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Lin LErnst M(2004)Improving the adaptability of multi-mode systems via program steeringACM SIGSOFT Software Engineering Notes10.1145/1013886.100754029:4(206-216)Online publication date: 1-Jul-2004
https://dl.acm.org/doi/10.1145/1013886.1007540
Lin LErnst MAvrunin GRothermel G(2004)Improving the adaptability of multi-mode systems via program steeringProceedings of the 2004 ACM SIGSOFT international symposium on Software testing and analysis10.1145/1007512.1007540(206-216)Online publication date: 11-Jul-2004
https://dl.acm.org/doi/10.1145/1007512.1007540
Haubelt CTeich JRichter KErnst R(2002)Flexibility/cost-tradeoffs of platform-based systemsEmbedded processor design challenges10.5555/765198.765202(38-56)Online publication date: 1-Jan-2002
https://dl.acm.org/doi/10.5555/765198.765202
Haubelt CTeich JRichter KErnst R(2002)Flexibility/Cost-Tradeoffs of Platform-Based SystemsEmbedded Processor Design Challenges10.1007/3-540-45874-3_3(38-56)Online publication date: 23-Apr-2002
https://doi.org/10.1007/3-540-45874-3_3
Strehl KThiele LGries MZiegenbein DErnst RTeich J(2001)FunState—an internal design representation for codesignIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/92.9312299:4(524-544)Online publication date: 1-Aug-2001
https://dl.acm.org/doi/10.1109/92.931229
Thiele LStrehl KZiegenbein DErnst RTeich JWhite JSentovich E(1999)FunState—an internal design representation for codesignProceedings of the 1999 IEEE/ACM international conference on Computer-aided design10.5555/339492.340077(558-565)Online publication date: 7-Nov-1999
https://dl.acm.org/doi/10.5555/339492.340077

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Chaotic dynamic weight particle swarm optimization for numerical function optimization

Function Call Optimization in Behavioral Synthesis

Development and investigation of efficient artificial bee colony algorithm for numerical function optimization