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Remembrance of circuits past: macromodeling by data mining in large analog design spaces
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Source Annual ACM IEEE Design Automation Conference archive
Proceedings of the 39th annual Design Automation Conference table of contents
New Orleans, Louisiana, USA
SESSION: Analog synthesis & design methodology table of contents
Pages: 437 - 442  
Year of Publication: 2002
ISBN ~ ISSN:0738-100X , 1-58113-461-4
Authors
Hongzhou Liu  Carnegie Mellon University, Pittsburgh, PA
Amit Singhee  Carnegie Mellon University, Pittsburgh, PA
Rob A. Rutenbar  Carnegie Mellon University, Pittsburgh, PA
L. Richard Carley  Carnegie Mellon University, Pittsburgh, PA
Sponsor
SIGDA: ACM Special Interest Group on Design Automation
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 4,   Downloads (12 Months): 21,   Citation Count: 29
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ABSTRACT

The introduction of simulation-based analog synthesis tools creates a new challenge for analog modeling. These tools routinely visit 103 to 105 fully simulated circuit solution candidates. What might we do with all this circuit data? We show how to adapt recent ideas from large-scale data mining to build models that capture significant regions of this visited performance space, parameterized by variables manipulated by synthesis, trained by the data points visited during synthesis. Experimental results show that we can automatically build useful nonlinear regression models for large analog design spaces.


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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CITED BY  29

Collaborative Colleagues:
Hongzhou Liu: colleagues
Amit Singhee: colleagues
Rob A. Rutenbar: colleagues
L. Richard Carley: colleagues