Statistical library characterization using belief propagation across multiple technology nodes
In this paper, we propose a novel flow to enable computationally efficient statistical characterization of delay and slew in standard cell libraries. The distinguishing feature of the proposed method is the usage of a limited combination of output capacitance, input slew rate and supply voltage for...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Association for Computing Machinery (ACM)
2015
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| Online Access: | http://hdl.handle.net/1721.1/96913 https://orcid.org/0000-0002-4836-6525 https://orcid.org/0000-0002-0417-445X |
| _version_ | 1826206742537568256 |
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| author | Yu, Li Saxena, Sharad Hess, Christopher Elfadel, Ibrahim M. Antoniadis, Dimitri A. Boning, Duane S. |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Yu, Li Saxena, Sharad Hess, Christopher Elfadel, Ibrahim M. Antoniadis, Dimitri A. Boning, Duane S. |
| author_sort | Yu, Li |
| collection | MIT |
| description | In this paper, we propose a novel flow to enable computationally efficient statistical characterization of delay and slew in standard cell libraries. The distinguishing feature of the proposed method is the usage of a limited combination of output capacitance, input slew rate and supply voltage for the extraction of statistical timing metrics of an individual logic gate. The efficiency of the proposed flow stems from the introduction of a novel, ultra-compact, nonlinear, analytical timing model, having only four universal regression parameters. This novel model facilitates the use of maximum-a-posteriori belief propagation to learn the prior parameter distribution for the parameters of the target technology from past characterizations of library cells belonging to various other technologies, including older ones. The framework then utilises Bayesian inference to extract the new timing model parameters using an ultra-small set of additional timing measurements from the target technology. The proposed method is validated and benchmarked on several production-level cell libraries including a state-of-the-art 14-nm technology node and a variation-aware, compact transistor model. For the same accuracy as the conventional lookup-table approach, this new method achieves at least 15x reduction in simulation runs. |
| first_indexed | 2024-09-23T13:37:33Z |
| format | Article |
| id | mit-1721.1/96913 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T13:37:33Z |
| publishDate | 2015 |
| publisher | Association for Computing Machinery (ACM) |
| record_format | dspace |
| spelling | mit-1721.1/969132022-10-01T16:07:02Z Statistical library characterization using belief propagation across multiple technology nodes Yu, Li Saxena, Sharad Hess, Christopher Elfadel, Ibrahim M. Antoniadis, Dimitri A. Boning, Duane S. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Yu, Li Antoniadis, Dimitri A. Boning, Duane S. In this paper, we propose a novel flow to enable computationally efficient statistical characterization of delay and slew in standard cell libraries. The distinguishing feature of the proposed method is the usage of a limited combination of output capacitance, input slew rate and supply voltage for the extraction of statistical timing metrics of an individual logic gate. The efficiency of the proposed flow stems from the introduction of a novel, ultra-compact, nonlinear, analytical timing model, having only four universal regression parameters. This novel model facilitates the use of maximum-a-posteriori belief propagation to learn the prior parameter distribution for the parameters of the target technology from past characterizations of library cells belonging to various other technologies, including older ones. The framework then utilises Bayesian inference to extract the new timing model parameters using an ultra-small set of additional timing measurements from the target technology. The proposed method is validated and benchmarked on several production-level cell libraries including a state-of-the-art 14-nm technology node and a variation-aware, compact transistor model. For the same accuracy as the conventional lookup-table approach, this new method achieves at least 15x reduction in simulation runs. Masdar Institute of Science and Technology (Massachusetts Institute of Technology Cooperative Agreement) 2015-05-05T16:49:36Z 2015-05-05T16:49:36Z 2015-03 Article http://purl.org/eprint/type/ConferencePaper 978-3-9815370-4-8 http://hdl.handle.net/1721.1/96913 Li Yu, Sharad Saxena, Christopher Hess, Ibrahim (Abe) M. Elfadel, Dimitri Antoniadis, and Duane Boning. 2015. Statistical library characterization using belief propagation across multiple technology nodes. In Proceedings of the 2015 Design, Automation & Test in Europe Conference & Exhibition (DATE '15). EDA Consortium, San Jose, CA, USA, 1383-1388. https://orcid.org/0000-0002-4836-6525 https://orcid.org/0000-0002-0417-445X en_US http://dl.acm.org/citation.cfm?id=2757012.2757134 Proceedings of the 2015 Design, Automation & Test in Europe Conference & Exhibition (DATE '15) Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Association for Computing Machinery (ACM) |
| spellingShingle | Yu, Li Saxena, Sharad Hess, Christopher Elfadel, Ibrahim M. Antoniadis, Dimitri A. Boning, Duane S. Statistical library characterization using belief propagation across multiple technology nodes |
| title | Statistical library characterization using belief propagation across multiple technology nodes |
| title_full | Statistical library characterization using belief propagation across multiple technology nodes |
| title_fullStr | Statistical library characterization using belief propagation across multiple technology nodes |
| title_full_unstemmed | Statistical library characterization using belief propagation across multiple technology nodes |
| title_short | Statistical library characterization using belief propagation across multiple technology nodes |
| title_sort | statistical library characterization using belief propagation across multiple technology nodes |
| url | http://hdl.handle.net/1721.1/96913 https://orcid.org/0000-0002-4836-6525 https://orcid.org/0000-0002-0417-445X |
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