@@ -50,4 +50,26 @@ After setting the [config.json](https://github.com/TILOS-AI-Institute/MacroPlace
python sa_multicore.py
```
## **Experimental Results**
We have tested our codes with the [ariane133](https://github.com/TILOS-AI-Institute/MacroPlacement/tree/main/CodeElements/SimulatedAnnealing/ariane133)(NanGate45, utilization = 0.68, clock_period = 1.3ns). Our configuration is as following:
***action_probs** : [0.2, 0.2, 0.2, 0.2, 0.2]
***num_actions(xn)** : 2
***max_temperature** : 5e-5
***num_iters** : 20000
***seed** : 1
***num_cores** : 8
***spiral_flag** : [False, True]
The cost curve is shown below. We can see that **Spiral placement** is better than **Greedy packer**.
<palign="center">
<imgsrc="./images/net_model.png"width="600"/>
</p>
<palign="center">
Figure 3. Illustration of net model used in Circuit Training.