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- Publisher Website: 10.1109/TETC.2014.2316503
- Scopus: eid_2-s2.0-84922290915
- WOS: WOS:000209844600008
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Article: Variation-aware layer assignment with hierarchical stochastic optimization on a multicore platform
Title | Variation-aware layer assignment with hierarchical stochastic optimization on a multicore platform |
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Authors | |
Keywords | Layer assignment stochastic programming. variation-aware design |
Issue Date | 2014 |
Citation | IEEE Transactions on Emerging Topics in Computing, 2014, v. 2, n. 4, p. 488-500 How to Cite? |
Abstract | As the very large scale integration (VLSI) technology enters the nanoscale regime, VLSI design is increasingly sensitive to variations on process, voltage, and temperature. Layer assignment technology plays a crucial role in industrial VLSI design flow. However, existing layer assignment approaches have largely ignored these variations, which can lead to significant timing violations. To address this issue, a variation-aware layer assignment approach for cost minimization is proposed in this paper. The proposed layer assignment approach is a single-stage stochastic program that directly controls the timing yield via a single parameter, and it is solved using Monte Carlo simulations and the Latin hypercube sampling technique. A hierarchical design is also adopted to enable the optimization process on a multicore platform. Experiments have been performed on 5000 industrial nets, and the results demonstrate that the proposed approach: 1) can significantly improve the timing yield by 64% in comparison with the nominal design and 2) can reduce the wire cost by 15.7% in comparison with the worst case design. |
Persistent Identifier | http://hdl.handle.net/10722/336133 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Chen, Xiaodao | - |
dc.contributor.author | Chen, Dan | - |
dc.contributor.author | Wang, Lizhe | - |
dc.contributor.author | Deng, Ze | - |
dc.contributor.author | Ranjan, Rajiv | - |
dc.contributor.author | Zomaya, Albert Y. | - |
dc.contributor.author | Hu, Shiyan | - |
dc.date.accessioned | 2024-01-15T08:23:46Z | - |
dc.date.available | 2024-01-15T08:23:46Z | - |
dc.date.issued | 2014 | - |
dc.identifier.citation | IEEE Transactions on Emerging Topics in Computing, 2014, v. 2, n. 4, p. 488-500 | - |
dc.identifier.uri | http://hdl.handle.net/10722/336133 | - |
dc.description.abstract | As the very large scale integration (VLSI) technology enters the nanoscale regime, VLSI design is increasingly sensitive to variations on process, voltage, and temperature. Layer assignment technology plays a crucial role in industrial VLSI design flow. However, existing layer assignment approaches have largely ignored these variations, which can lead to significant timing violations. To address this issue, a variation-aware layer assignment approach for cost minimization is proposed in this paper. The proposed layer assignment approach is a single-stage stochastic program that directly controls the timing yield via a single parameter, and it is solved using Monte Carlo simulations and the Latin hypercube sampling technique. A hierarchical design is also adopted to enable the optimization process on a multicore platform. Experiments have been performed on 5000 industrial nets, and the results demonstrate that the proposed approach: 1) can significantly improve the timing yield by 64% in comparison with the nominal design and 2) can reduce the wire cost by 15.7% in comparison with the worst case design. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Emerging Topics in Computing | - |
dc.subject | Layer assignment | - |
dc.subject | stochastic programming. | - |
dc.subject | variation-aware design | - |
dc.title | Variation-aware layer assignment with hierarchical stochastic optimization on a multicore platform | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TETC.2014.2316503 | - |
dc.identifier.scopus | eid_2-s2.0-84922290915 | - |
dc.identifier.volume | 2 | - |
dc.identifier.issue | 4 | - |
dc.identifier.spage | 488 | - |
dc.identifier.epage | 500 | - |
dc.identifier.eissn | 2168-6750 | - |
dc.identifier.isi | WOS:000209844600008 | - |