Incremental component implementation selection: enabling ECO in compositional system synthesis
Proceedings of the 2007 IEEE/ACM international conference on Computer-aided design
Timing budgeting under arbitrary process variations
Proceedings of the 2007 IEEE/ACM international conference on Computer-aided design
Gate sizing for large cell-based designs
Proceedings of the Conference on Design, Automation and Test in Europe
Simultaneous slack budgeting and retiming for synchronous circuits optimization
Proceedings of the 2010 Asia and South Pacific Design Automation Conference
On incremental component implementation selection in system synthesis
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Network flow-based simultaneous retiming and slack budgeting for low power design
Proceedings of the 16th Asia and South Pacific Design Automation Conference
Progress and challenges in VLSI placement research
Proceedings of the International Conference on Computer-Aided Design
Slack budgeting and slack to length converting for multi-bit flip-flop merging
Proceedings of the Conference on Design, Automation and Test in Europe
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This paper presents a theoretical framework that solves optimally and in polynomial time many open problems in time budgeting. The approach unifies a large class of existing time-management paradigms. Examples include time budgeting for maximizing total weighted delay relaxation, minimizing the maximum relaxation, and min-skew time budget distribution. The authors develop a combinatorial framework through which we prove that many of the time-management problems can be transformed into a min-cost flow problem instance. The methodology is applied to intellectual-property-based datapath synthesis targeting field-programmable gate arrays. The synthesis flow maps the input operations to parameterized library modules during which different time budgeting policies have been applied. The techniques always improve the area requirement of the implemented test benches and consistently outperform a widely used competitor. The experiments verify that combining fairness and maximization objectives improves the results further as compared with pure maximum budgeting. The combined fairness and maximization objective improves the area by 25.8% and 28.7% in slice and LUT counts, respectively