@@ -5291,7 +5291,7 @@ We thank NVIDIA Research for access to AutoDMP, an autotuned DREAMPlace-based ma
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**<span style="color:blue">Question 11.</span>** How does the initial placement generated by different physical synthesis tools affect the CT solution?
We observe that when the initial placement solution is generated using [Flow-2](https://github.com/TILOS-AI-Institute/MacroPlacement/blob/main/Flows/figures/flow-2.PNG)(CMP-Genus iSpatial) or DC-Topo ([links](../../Flows/scripts/DCTopoFlow/) to scripts) the final CT outcomes are similar.
We observe that whether the initial placement solution is generated using [Flow-2](https://github.com/TILOS-AI-Institute/MacroPlacement/blob/main/Flows/figures/flow-2.PNG)(CMP-Genus iSpatial) or the initial placement is generated by DC-Topo ([links](../../Flows/scripts/DCTopoFlow/) to scripts), the final CT outcomes are similar.
The following table and screenshots provide details of Ariane133-NG45-68%-1.3ns CT macro placement when DC-Topo is used to generate the initial placement solution.
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@@ -5374,7 +5374,7 @@ The following table and screenshots provide details of Ariane133-NG45-68%-1.3ns
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**<span style="color:blue">Question 12.</span>** How well does Simulated Annealing (SA) optimize the proxy cost?
Details of our SA implementation, which we denote as SA-UCSD, are [here](../../CodeElements/SimulatedAnnealing/). We have used SA-UCSD to generate macro placements for Ariane and BlackParrot (Quad-Core).
Details of our SA implementation, which we denote as SA-UCSD, are [here](../../CodeElements/SimulatedAnnealing/). We have used SA-UCSD to generate macro placements for Ariane and BlackParrot (Quad-Core). We find that SA-UCSD produces better proxy costs than CT.
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-**Ariane133-NG45-68%-1.3ns**: The configuration that results best proxy cost (wirelength cost: 0.0881, congestion cost: 0.8257, density cost: 0.5084, proxy cost: 0.75515): *action_probs: [0.2, 0.2, 0.2, 0.2, 0.2], num_actions: 3, max_temperature: 7e-5, num_iters: 50000, seed: 1, spiral_flag: True*