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Network flow-based power optimization under timing constraints in MSV-driven floorplanning
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International Conference on Computer Aided Design archive
Proceedings of the 2008 IEEE/ACM International Conference on Computer-Aided Design table of contents
San Jose, California
SESSION: Floorplanning table of contents
Pages 1-8  
Year of Publication: 2008
ISBN ~ ISSN:1092-3152 , 978-1-4244-2820-5
Authors
Qiang Ma  The Chinese University of Hong Kong
Evangeline F. Y. Young  The Chinese University of Hong Kong
Sponsors
: IEEE CASS/CANDE
: IEEE Council on Electronic Design Automation (CEDA)
SIGDA: ACM Special Interest Group on Design Automation
Publisher
IEEE Press  Piscataway, NJ, USA
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Downloads (6 Weeks): 7,   Downloads (12 Months): 71,   Citation Count: 2
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ABSTRACT

Power consumption has become a crucial problem in modern circuit design. Multiple Supply Voltage (MSV) design is introduced to provide higher flexibility in controlling the power and performance tradeoff. One important requirement of MSV design is that timing constraints of the circuit must be satisfied after voltage assignment of the cells. In this paper, we will show that the voltage assignment task on a given netlist can be formulated as a convex cost dual network flow problem and can be solved optimally in polynomial time using a cost-scaling algorithm when the delay choices of each module are continuous in the real or integer domain. We can make use of this approach to obtain a feasible voltage assignment solution in the general cases with power consumption approximating the minimum one. Furthermore, we will propose a framework to optimize power consumption and physical layout of a circuit simultaneously during the floorplanning stage, by embedding this cost-scaling solver into a simulated annealing based floorplanner. This is effective in practice due to the short running time of the solver. We compared our approach with the latest work [9] on the same problem, and the experimental results show that, using our framework, significant improvement on power saving (18% less power cost on average) can be achieved in much less running time (7X faster on average) for all the test cases, which confirms the effectiveness of our approach.


REFERENCES

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Collaborative Colleagues:
Qiang Ma: colleagues
Evangeline F. Y. Young: colleagues