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Multiprocessor performance estimation using hybrid simulation
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Source Annual ACM IEEE Design Automation Conference archive
Proceedings of the 45th annual Design Automation Conference table of contents
Anaheim, California
SESSION: Analytical modeling and simulation of complex processing systems table of contents
Pages 325-330  
Year of Publication: 2008
ISBN ~ ISSN:0738-100X , 978-1-60558-115-6
Authors
Lei Gao  RWTH Aachen University, Germany
Kingshuk Karuri  RWTH Aachen University, Germany
Stefan Kraemer  RWTH Aachen University, Germany
Rainer Leupers  RWTH Aachen University, Germany
Gerd Ascheid  RWTH Aachen University, Germany
Heinrich Meyr  RWTH Aachen University, Germany
Sponsors
SIGDA: ACM Special Interest Group on Design Automation
: IEEE/CASS/CANDE/CEDA
: The EDA Consortium
Publisher
ACM  New York, NY, USA
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ABSTRACT

With the growing number of programmable processing elements in today's Multi Processor System-on-Chip (MPSoC) designs, the synergy required for the development of the hardware architecture and the software running on them is also increasing. In MPSoC development environment, changes in the hardware architecture can bring in extensive re-partitioning or re-parallelization of the software architecture. Fast and accurate functional simulation and performance estimation techniques are needed to cope with this co-design problem at the early phases of MPSoC design space exploration. The current paper addresses this issue by introducing a framework which combines hybrid simulation, cache simulation and online trace-driven replay techniques to accurately predict performance of programmable elements in an MPSoC environment. The resulting simulation technique can easily cope with the continuous re-organizations of software architectures during an Instruction Set Simulator (ISS) based design process. Experimental results show that this framework can improve system simulation speed by 3-5× on average while achieving accuracy closely comparable to traditional ISSes.


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:
Lei Gao: colleagues
Kingshuk Karuri: colleagues
Stefan Kraemer: colleagues
Rainer Leupers: colleagues
Gerd Ascheid: colleagues
Heinrich Meyr: colleagues