Merge branch 'main' of github.com:TILOS-AI-Institute/MacroPlacement into flow_scripts

CodeElements/Clustering/README.md

@@ -113,10 +113,11 @@ In this step, Circuit Training recursively merges small clusters to the most adj
 distance *closeness* (*breakup_threshold* / 2.0),  thus reducing number of clusters.  A cluster is claimed as a small cluster 
 if the number of elements (macro pins, 
 macros, IO ports and standard cells) is less than or equal to *max_num_nodes*, where *max_num_nodes* = *number_of_vertices* // *number_of_clusters_after_breakup* // 4.  The merging process is as following:
-* flag = True
-* While (flag == True):
+* flag = False
+* While (flag == False):
    * Create adjacency matrix *adj_matrix* where *adj_matrix\[i\]\[j\]* represents the number of connections between cluster *c<sub>i</sub>* and cluster *c<sub>j</sub>*. For example, in the Figure 1, suppose *A*, *B*, *C*, *D* and *E* respectively belong to cluster *c<sub>1</sub>*, ..., *c<sub>5</sub>*, we have *adj_matrix\[1\]\[2\]* = 1, *adj_matrix\[1\]\[3\]* = 1, ...., *adj_matrix\[5\]\[3\]* = 1 and *adj_matrix\[5\]\[4\]* = 1. We want to emphasize that although there is no hyperedges related to macros in the hypergraph, *adj_matrix* considers the "virtual" connections between macros and macro pins. That is to say, if a macro and its macros pins belong to different clusters, for example, macro A in cluster *c<sub>1</sub>* and its macro pins in cluster *c<sub>2</sub>*, we have *adj_matrix\[1\]\[2\]* = 1 and *adj_matrix\[2\]\[1\]* = 1.
    * Calculate the weighted center for each cluster. (see the breakup section for details)
+   * flag = True
    * For each cluster *c*
       * If *c* is not a small cluster
          * Continue

Docs/README.md

 # Links to Our Documents
-- [Our Progress](./OurProgress/)  
-- [Proxy Cost](./ProxyCost/)
-- [Code Elements](./CodeElements/)
\ No newline at end of file
+- [Our Progress: A Chronology](./OurProgress/) provides latest updates and is periodically synched to [this Google Doc](https://docs.google.com/document/d/1HHZNcid5CZvvRqj_njzF7hBhtNSpmRn3fCYniWNYBiY/edit).
+- [Proxy Cost](./ProxyCost/) documentation gives implementation details to enable reproduction of the wirelength, density and congestion costs used by [Circuit Training](https://github.com/google-research/circuit_training).
+- [Code Elements](./CodeElements/) documentation gives implementation details to enable reproduction of the gridding, gruoping and clustering used by [Circuit Training](https://github.com/google-research/circuit_training).

README.md

@@ -4,6 +4,7 @@
 ## **Our Latest Progress**
  - [Our Progress: A Chronology](https://tilos-ai-institute.github.io/MacroPlacement/Docs/OurProgress/) provides latest updates and is periodically synched to [this Google Doc](https://docs.google.com/document/d/1HHZNcid5CZvvRqj_njzF7hBhtNSpmRn3fCYniWNYBiY/edit).
  - Our [Proxy Cost](https://tilos-ai-institute.github.io/MacroPlacement/Docs/ProxyCost/) documentation gives implementation details to enable reproduction of the wirelength, density and congestion costs used by [Circuit Training](https://github.com/google-research/circuit_training).
+ - Our [Code Elements](https://tilos-ai-institute.github.io/MacroPlacement/Docs/CodeElements/) documentation gives implementation details to enable reproduction of the gridding, grouping and clustering used by [Circuit Training](https://github.com/google-research/circuit_training).
 
 ## **Table of Contents**
   <!-- - [Reproducible Example Solutions](#reproducible-example-solutions) -->