Publication | Closed Access
Dataflow-Aware Macro Placement Based on Simulated Evolution Algorithm for Mixed-Size Designs
18
Citations
17
References
2021
Year
Cluster ComputingEngineeringElectronic Design AutomationNetwork RoutingComputer ArchitectureSoftware EngineeringData StructureEvolutionary Multimodal OptimizationSimulated AnnealingScalable RoutingModeling And SimulationParallel ComputingCombinatorial OptimizationAdvanced NetworkingSimulated Evolution AlgorithmDesign Space ExplorationMacro PlacementDesignComputer EngineeringComputer ScienceEvolutionary ProgrammingNetwork Routing AlgorithmEdge ComputingCloud ComputingMixed-size DesignsParallel ProgrammingDataflow-aware Macro PlacementEvolutionary Design
This article proposes a novel approach to handle macro placement. Previous works usually apply the simulated annealing (SA) algorithm to handle this problem. However, the SA-based approaches usually have difficulty in handling preplaced macros and require longer runtime. To resolve these problems, we propose a macro placement procedure based on the corner stitching data structure and then apply an efficient and effective simulated evolution algorithm to further refine placement results. In order to relieve local routing congestion, we propose to expand areas of movable macros according to the design hierarchy before applying the macro placement algorithm. Finally, we extend our macro placement methodology to consider dataflow constraint so that dataflow-related macros can be placed at close locations. The experimental results show that our approach obtains a better solution than a previous macro placement algorithm and a tool. Besides, placement quality can be further improved when the dataflow constraint is considered.
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