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Microarchitectural power modeling techniques for deep sub-micron microprocessors
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International Symposium on Low Power Electronics and Design archive
Proceedings of the 2004 international symposium on Low power electronics and design table of contents
Newport Beach, California, USA
SESSION: High level power modeling and analysis table of contents
Pages: 212 - 217  
Year of Publication: 2004
ISBN:1-58113-929-2
Authors
Nam Sung Kim  Intel Labs, Hillsboro, OR
Taeho Kgil  University of Michigan, Ann Arbor, MI
Valeria Bertacco  University of Michigan, Ann Arbor, MI
Todd Austin  University of Michigan, Ann Arbor, MI
Trevor Mudge  University of Michigan, Ann Arbor, MI
Sponsors
ACM: Association for Computing Machinery
SIGDA: ACM Special Interest Group on Design Automation
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 3,   Downloads (12 Months): 51,   Citation Count: 2
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ABSTRACT

The need to perform early design studies that combine architectural simulation with power estimation has become critical as power has become a design constraint whose importance has moved to the fore. To satisfy this demand several microarchitectural power simulators have been developed around SimpleScalar, a widely used microarchitectural performance simulator. They have proven to be very useful at providing insights into power/performance trade-offs. However, they are neither parameterized nor technology scalable. In this paper, we propose more accurate parameterized power modeling techniques reflecting the actual technology parameters as well as input switching-events for memory and execution units. Compared to HSPICE, the proposed techniques show 93% and 91% accuracies for those blocks, but with a much faster simulation time. We also propose a more realistic power modeling technique for external I/O. In general, our approach includes more detailed microarchitectural and circuit modeling than has been the case in earlier simulators, without incurring a significant simulation time overhead--it can be as small as a few percent.


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:
Nam Sung Kim: colleagues
Taeho Kgil: colleagues
Valeria Bertacco: colleagues
Todd Austin: colleagues
Trevor Mudge: colleagues