Sequence-pair based placement method for hard/soft/pre-placed modules

Murata, Hiroshi; Kuh, E.S.

doi:10.1145/274535.274560

Cited by 81 publications

(56 citation statements)

References 11 publications

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“…Moh et al [10] formulated the shaping problem as a geometric programming and searched for the optimal floorplan area using standard convex optimization. Following the same framework as in [10], Murata et al [11] improved the algorithm efficiency via reducing the number of variables and functions. But the algorithm still took a long time to find a good solution.…”

Section: A Previous Workmentioning

confidence: 99%

SDS: An Optimal Slack-Driven Block Shaping Algorithm for Fixed-Outline Floorplanning

Yan

Chu

2013

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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Abstract-This paper presents an efficient, scalable, and optimal slack-driven shaping algorithm for soft blocks in nonslicing floorplan. The proposed algorithm is called SDS. SDS is specifically formulated for fixed-outline floorplanning. Given a fixed upper bound on the layout width, SDS minimizes the layout height by only shaping the soft blocks in the design. Iteratively, SDS shapes some soft blocks to minimize the layout height with the guarantee that the layout width would not exceed the given upper bound. Rather than using some simple heuristic as in previous work, the amount of change on each block is determined by systematically distributing the global total amount of available slack to individual block. During the whole shaping process, the layout height monotonically reduces and eventually converges to an optimal solution. Two optimality conditions are presented to check the optimality of a shaping solution for fixedoutline floorplanning. In practice, to terminate the process of convergence early, we propose two different stopping criteria. We also extend SDS to handle other floorplanning problems, e.g., classical floorplanning. To validate the efficiency and effectiveness of SDS, comprehensive experiments are conducted on MCNC and HB benchmarks. Compared with previous work, SDS achieves the best experimental result with a significantly faster runtime.Index Terms-Block shaping, fixed-outline floorplanning, very large scale integration physical design.

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Section: A Previous Workmentioning

confidence: 99%

SDS: An Optimal Slack-Driven Block Shaping Algorithm for Fixed-Outline Floorplanning

Yan

Chu

2013

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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“…Moh et al [9] formulated the shaping problem as a geometric programming and searched for the optimal floorplan area using standard convex optimization. Following the same framework as in [9], Murata et al [10] improved the algorithm efficiency via reducing the number of variables and functions. But the algorithm still took a long time to find a good solution.…”

Section: Previous Workmentioning

confidence: 99%

Optimal slack-driven block shaping algorithm in fixed-outline floorplanning

Yan

Chu

2012

Proceedings of the 2012 ACM International Symposium on International Symposium on Physical Design

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This paper presents an efficient, scalable and optimal slack-driven shaping algorithm for soft blocks in non-slicing floorplan. The proposed algorithm is called SDS. Different from all previous approaches, SDS is specifically formulated for fixed-outline floorplanning. Given a fixed upper bound on the layout width, SDS minimizes the layout height by only shaping the soft blocks in the design. Iteratively, SDS shapes some soft blocks to minimize the layout height, with the guarantee that the layout width would not exceed the given upper bound. Rather than using some simple heuristic as in previous work, the amount of change on each block is determined by systematically distributing the global total amount of available slack to individual block. During the whole shaping process, the layout height is monotonically reducing, and eventually converges to an optimal solution. We also propose two optimality conditions to check the optimality of a shaping solution. To validate the efficiency and effectiveness of SDS, comprehensive experiments are conducted on MCNC and HB benchmarks. Compared with previous work, SDS is able to achieve the best experimental result with significantly faster runtime.

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“…[MK98] uses a transformation technique to construct a convex optimization problem with convex constraints. Although these techniques are useful in simplifying the area constraints (7.1), they require an HV-constraint graph pair be provided as input, and so are not complete solutions to the floorplanning problem in themselves.…”

Section: Existing Workmentioning

confidence: 99%

Integration of Physical Design and Sequential Optimization

Chong¹

2006

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Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. This work examines the interaction between the physical design of digital integrated circuits and sequential optimization techniques used for performance enhancement. In particular, the integration of floorplanning and placement with retiming and clock skew scheduling is explored. A theoretical result is given which addresses the computational complexity of circuit partitioning under constraints derived from sequential optimization; this motivates the need for heuristic approaches to the related placement problem. Another theoretical result provides a characterization of the feasible retimings of a sequential circuit; this result is used to motivate an effective method for floorplanning integrated with sequential optimization. Practical techniques for using sequential slack to drive standard-cell placement are shown here; experiments demonstrate significant improvement in final design performance using these methods. Another part of this work examines how the role of sequential optimization and physical design changes when the design allows for asynchronous or latency-insensitive communication between modules. A theoretical result relating to the problem of clock tree implementation for clock skew scheduling under process variation is given. Finally an experimental technique for floorplanning using nonlinear programming is demonstrated. All rights reserved.Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission. Integration of Physical Design and Sequential

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Sequence-pair based placement method for hard/soft/pre-placed modules

Cited by 81 publications

References 11 publications

SDS: An Optimal Slack-Driven Block Shaping Algorithm for Fixed-Outline Floorplanning

SDS: An Optimal Slack-Driven Block Shaping Algorithm for Fixed-Outline Floorplanning

Optimal slack-driven block shaping algorithm in fixed-outline floorplanning

Integration of Physical Design and Sequential Optimization

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