**Hypergraph clustering** is, in our view, one of the most crucial undocumented
portions of Circuit Training.
## **I. Information provided by Google.**
## **Information provided by Google.**
The Methods section of the [Nature paper](https://www.nature.com/articles/s41586-021-03544-w.epdf?sharing_token=tYaxh2mR5EozfsSL0WHZLdRgN0jAjWel9jnR3ZoTv0PW0K0NmVrRsFPaMa9Y5We9O4Hqf_liatg-lvhiVcYpHL_YQpqkurA31sxqtmA-E1yNUWVMMVSBxWSp7ZFFIWawYQYnEXoBE4esRDSWqubhDFWUPyI5wK_5B_YIO-D_kS8%3D) provides the following information.
* “(1) We group millions of standard cells into a few thousand clusters using hMETIS, a partitioning technique based
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@@ -44,14 +44,14 @@ Finally, the Methods section of the [Nature paper](https://www.nature.com/articl
## **II. What *exactly* is the Hypergraph, and how is it partitioned?**
## **What *exactly* is the Hypergraph, and how is it partitioned?**
From the above information sources, the description of the [Grouping](https://github.com/TILOS-AI-Institute/MacroPlacement/blob/main/CodeElements/Grouping/README.md) process, and information provided by Google engineers, we are fairly certain of the following.
* (1) Clustering uses the hMETIS partitioner, which is run in “multiway” mode.
More specifically, hMETIS is **always** invoked with *npart* more than 500, with unit vertex weights.
The hyperparameters given in Extended Data Table 3 of the [Nature paper](https://www.nature.com/articles/s41586-021-03544-w.epdf?sharing_token=tYaxh2mR5EozfsSL0WHZLdRgN0jAjWel9jnR3ZoTv0PW0K0NmVrRsFPaMa9Y5We9O4Hqf_liatg-lvhiVcYpHL_YQpqkurA31sxqtmA-E1yNUWVMMVSBxWSp7ZFFIWawYQYnEXoBE4esRDSWqubhDFWUPyI5wK_5B_YIO-D_kS8%3D) are used.
(Additionally, Circuit Training explicitly sets reconst=1 and dbglvl=0.)
* (2) The hypergraph that is fed to hMETIS consists of macros, macro pins, IO ports and standard cells.
* (2) The hypergraph that is fed to hMETIS consists of macros, macro pins, IO ports, and standard cells.
The "fixed" file generated by [Grouping](https://github.com/TILOS-AI-Institute/MacroPlacement/blob/main/CodeElements/Grouping/README.md) process, is also fed as .fix input file to hMETIS.
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@@ -74,64 +74,64 @@ in these clusters corresponds to an entry of the .fix file. The cluster id start
* The number of individual standard cells in the hypergraph that is actually partitioned by hMETIS is 200,000 - (100 * 300) - (20 * 50) = 169,000.
* Suppose that each macro has 64 macro pins. The hypergraph that is actually partitioned by hMETIS has
200,000 + 100 + 1000 + 100 * 64 = 207, 500 vertices. Although there are both macro pins and macros in the hypergraph, all the nets related to macros are connected to macro pins and there is no hyperedges related to macros. Each hyperedge in the hypergraph cooresponds to a net in the netlist. Note that Circuit Training assumes that there is only one output pin for each standard cell, thus there is only one hyperedge {**A**, **B**, **C**, **D**, **E**} for the following case.
200,000 + 100 + 1000 + 100 * 64 = 207,500 vertices. Although there are both macro pins and macros in the hypergraph, all the nets related to macros are connected to macro pins and there are no hyperedges incident to macros. Each hyperedge in the hypergraph corresponds to a net in the netlist. Note that Circuit Training assumes that there is only one output pin for each standard cell, thus there is only one hyperedge {**A**, **B**, **C**, **D**, **E**} for the following case.
<palign="center">
<imgsrc="./net_model.png"width="600"/>
<imgsrc="./images/net_model.png"width="600"/>
</p>
<palign="center">
Figure 1. Illustration of net model in Circuit Training.
Figure 3. Illustration of net model used in Circuit Training.
</p>
**nparts* = 500 + 120 = 620 is used when applying hMETIS to this hypergraph.
## **III. Break up clusters that span a distance larger than *breakup_threshold***
## **Break up clusters that span a distance larger than *breakup_threshold***
After partitioning the hypergraph, we can have *nparts* clusters.
Then Circuit Training break up clusters that span a distance larger than *breakup_threshold*.
Then Circuit Training breaks up clusters that span a distance larger than *breakup_threshold*.
Here *breakup_threshold = sqrt(canvas_width * canvas_height / 16)*.
For each cluster *c*, the breakup process is as following:
For each cluster *c*, the breakup process is as follows:
**cluster_x, cluster_y = c.GetWeightedCenter()*. Here the weighted center of cluster *c* is the average location of all the standard cells in the cluster, weighted according to their area.
*Use (*cluster_x*, *cluster_y*) as the origin and *breakup_threshold* as the step, to divide the bounding box of *c* into different regions.
*The elements (macro pins, macros, ports and standard cells) in each region form a new cluster.
The following figure shows an example: the left part shows the cluster *c<sub>1</sub>* before breakup process and the blue dot is the weighted center of *c<sub>1</sub>*; the right part shows the clusters after breakupup process. The "center" cluster still has the cluster id of 1.
**cluster_x, cluster_y = c.GetWeightedCenter()*. Here the weighted center of cluster *c* is the average location of all the *standard cells* in the cluster, weighted according to their area.
*use (*cluster_x*, *cluster_y*) as the origin and *breakup_threshold* as the step, to divide the bounding box of *c* into different regions.
*the elements (macro pins, macros, ports and standard cells) in each region form a new cluster.
The following figure shows an example: the left part shows the cluster *c<sub>1</sub>* before the breakup process, and the blue dot is the weighted center of *c<sub>1</sub>*; the right part shows the clusters after the breakup process. The "center" cluster still has the cluster id of 1.
<palign="center">
<imgsrc="./breakup.png"width="1600"/>
<imgsrc="./images/breakup.png"width="1600"/>
</p>
<palign="center">
Figure 2. Illustration of breaking up a cluster.
Figure 4. Illustration of breaking up a cluster.
</p>
Note that the netlist is generated by physical-aware synthesis, we know the (x, y) coordinate for each instance.
Note that since the netlist is generated by physical-aware synthesis, we know the (x, y) coordinate for each instance.
## **IV. Recursively merge small adjacent clusters**
## **Recursively merge small adjacent clusters**
After breaking up clusters which span large distance, there may be some small clusters with only tens of standard cells.
In this step, Circuit Training recursively merges small clusters to the most adjacent cluster if they are within a certain
distance *closeness* (*breakup_threshold* / 2.0), thus reducing number of clusters. A cluster is claimed as a small cluster
distance *closeness* (*breakup_threshold* / 2.0), thus reducing number of clusters. A cluster is defined to be 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 = 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)
*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 are no hyperedges incident 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 "Break Up Clusters" above for details.)
* flag = True
*For each cluster *c*
*If *c* is not a small cluster
*for each cluster *c*
*if *c* is not a small cluster
* Continue
*Find all the clusters *close_clusters* which is close to *c*, i.e., the Manhattan distance between their weighted centers and the weighted center of *c* is less than or equal to *closeness*
*If there is no clusters close to *c*
*find all the clusters *close_clusters* which are close to *c*, i.e., the Manhattan distance between their weighted centers and the weighted center of *c* is less than or equal to *closeness*
*if there is no cluster close to *c*
* Continue
*Find the most adjacent cluster *adj_cluster* of *c* in *close_clusters*, i.e., maximize *adj_matrix\[c\]\[adj_cluster\]*
*Merge *c* to *adj_cluster*
*If *adj_cluster* is a small cluster
*find the most adjacent cluster *adj_cluster* of *c* in *close_clusters*, i.e., maximize *adj_matrix\[c\]\[adj_cluster\]*
*merge *c* to *adj_cluster*
*if *adj_cluster* is a small cluster
* flag = False
## **V. Pending Clarifications**
## **Pending Clarifications**
We call readers’ attention to the existence of significant aspects that are still pending clarification here.
While [Gridding](https://github.com/TILOS-AI-Institute/MacroPlacement/blob/main/CodeElements/Gridding/README.md) and
[Grouping](https://github.com/TILOS-AI-Institute/MacroPlacement/blob/main/CodeElements/Grouping/README.md) are hopefully well-understood,
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@@ -141,17 +141,17 @@ we are still in the process of documenting and implementing such aspects as the
All methodologies that span synthesis and placement (of which we are aware) must make a fundamental decision with respect to the netlist that is produced by logic synthesis, as that netlist is passed on to placement: (A) delete buffers and inverters to avoid biasing the ensuing placement (spatial embedding) with the synthesis tool’s fanout clustering, or (B) leave these buffers and inverters in the netlist to maintain netlist area and electrical rules (load, fanout) sensibility. We do not yet know Google’s choice in this regard. Our experimental runscripts will therefore support both (A) and (B).
***[June 13]*****Update to Pending clarification #3:*** We are glad to see [grouping (clustering)](https://github.com/google-research/circuit_training/tree/main/circuit_training/grouping) added to the Circuit Training GitHub. The new scripts refer to (x,y) coordinates of nodes in the netlist, which leads to further pending clarifications (noted [here](https://github.com/google-research/circuit_training/issues/25)). The solution space for how the input to hypergraph clustering is obtained has expanded. A first level of options is whether **(A) a non-physical synthesis tool** (e.g., Genus, DesignCompiler or Yosys), or **(B) a physical synthesis tool** (e.g., Genus iSpatial or DesignCompiler Topological (Yosys cannot perform physical synthesis)), is used to obtain the netlist from starting RTL and constraints. In the regime of (B), to our understanding the commercial physical synthesis tools are invoked with a starting .def that includes macro placement. Thus, we plan to also enable a second level of sub-options for determining this macro placement: **(B.1)** use the auto-macro placement result from the physical synthesis tool, and **(B.2)** use a human PD expert (or, [OpenROAD RTL-MP](https://github.com/The-OpenROAD-Project/OpenROAD/tree/master/src/mpl2)) macro placement.
***[June 13]*****Update to Pending clarification #3:*** We are glad to see [grouping (clustering)](https://github.com/google-research/circuit_training/tree/main/circuit_training/grouping) added to the Circuit Training GitHub. The new scripts refer to (x,y) coordinates of nodes in the netlist, which leads to further pending clarifications (noted [here](https://github.com/google-research/circuit_training/issues/25)). The solution space for how the input to hypergraph clustering is obtained has expanded. A first level of options is whether **(A) a non-physical synthesis tool** (e.g., Genus, DesignCompiler or Yosys), or **(B) a physical synthesis tool** (e.g., Genus iSpatial or DesignCompiler Topological (Yosys cannot perform physical synthesis)), is used to obtain the netlist from starting RTL and constraints. In the regime of (B), to our understanding the commercial physical synthesis tools are invoked with a starting .def that includes macro placement. Thus, we plan to also enable a second level of sub-options for determining this macro placement: **(B.1)** use the auto-macro placement result from the physical synthesis tool, and **(B.2)** use a human PD expert (or, [OpenROAD RTL-MP](https://github.com/The-OpenROAD-Project/OpenROAD/tree/master/src/mpl2)) macro placement. Some initial progress toward these clarifications has been posted as [Our Progress](https://github.com/TILOS-AI-Institute/MacroPlacement/tree/main/Docs/OurProgress).
## **VI. Our Implementation of Hypergraph Clustering.**
## **Our Implementation of Hypergraph Clustering.**
Our implementation of hypergraph clustering takes the synthesized netlist and a .def file with placed IO ports as input,
then generates the clustered netlist (in lef/def format) using hMETIS (1998 binary).
In default mode, our implementation will generate the clustered netlist in protocol buffer format and cooresponding plc file.
We implement the entire flow based on [OpenROAD APIs](https://github.com/the-openroad-project).
**Please refer to [the OpenROAD repo](https://github.com/the-openroad-project) for explanation of each Tcl command.**
We implement the entire flow based on [OpenROAD APIs](https://github.com/ravi-varadarajan/OpenROAD.git).
**Please refer to [the OpenROAD repo](https://github.com/ravi-varadarajan/OpenROAD.git) for explanation of each Tcl command.**
Please note that [The OpenROAD Project](https://github.com/the-openroad-project) does not
Please note that [The OpenROAD Project](https://github.com/ravi-varadarajan/OpenROAD.git) does not
distribute any compiled binaries. You need to build your own OpenROAD binary before you run our scripts.
Input file: [setup.tcl](https://github.com/TILOS-AI-Institute/MacroPlacement/blob/main/CodeElements/Clustering/test/setup.tcl)(you can follow the example to set up your own design) and [FixFile](https://github.com/TILOS-AI-Institute/MacroPlacement/blob/main/CodeElements/Clustering/test/fix_files_grouping/ariane.fix.old)(This file is generated by our [Grouping](https://github.com/TILOS-AI-Institute/MacroPlacement/tree/main/CodeElements/Grouping) scripts)
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@@ -160,6 +160,8 @@ Output_files: the clustered netlist in protocol buffer format and cooresponding
Note that the [example](https://github.com/TILOS-AI-Institute/MacroPlacement/tree/main/CodeElements/Clustering/test) that we provide is the ariane design implemented in NanGate45. The netlist and corresponding def file with placed instances are generated by [Genus iSpatial](https://github.com/TILOS-AI-Institute/MacroPlacement/tree/main/Flows/NanGate45/ariane133) flow. Here the macro placement is automatically done by the Genus and Innovus tools,
In Circuit Training, the purpose of gridding is to control the size of the macro placement solution space, thus allowing RL to train within reasonable
runtimes. Gridding enables hard macros to find locations consistent with high solution quality, while allowing soft macros (standard-cell clusters) to also find good locations.
Gridding determines a dissection of the layout canvas into some number of rows (**n_rows**) and some number of columns (**n_cols**) of _gridcells_.
Gridding determines a dissection of the layout canvas into some number of rows (*n_rows*) and some number of columns (*n_cols*) of _gridcells_.
The choice of **n_rows** and **n_cols** is made **once** for each design. Once the dimensions **(n_rows, n_cols)** have been chosen, their values define a gridded canvas, or _grid_, and remain fixed throughout Circuit Training for the given design. The detailed algorithm is shown as following.
The choice of *n_rows* and *n_cols* is made **once** for each design. Once the dimensions *(n_rows, n_cols)* have been chosen, their values define a gridded canvas, or _grid_, and remain fixed throughout Circuit Training for the given design. The detailed algorithm is shown as following.
The gridding algorithm starts with the dimensions **canvas_width** and **canvas_height** of the layout canvas, as well as a list of **macros**, where each macro has a width and a height.
The gridding algorithm starts with the dimensions *canvas_width* and *canvas_height* of the layout canvas, as well as a list of *macros*, where each macro has a width and a height.
Macros are not rotatable. The area of a macro is the product of its width and height.
Then, the gridding searches over combinations (**n_rows**, **n_cols**), with constraints
- The macros can be packed sequentially on the gridcells. There are *n_rows * n_cols* gridcells in the canvas. \[Algorithm 1 Lines 11-22\]
where each gridcell has width of *grid_w = canvas_width / n_cols*
and height of *grid_h = canvas_height / n_row*.
The main idea is to search for a particular (*n_rows*, *n_cols*) combination
that maximize the metric related to wasted space.
To evaluate metric for a given _grid_ (**n_rows**, **n_cols**),
To evaluate *metric* for a given _grid_ (*n_rows*, *n_cols*),
all macros are packed into the _gridcells_,
and several terms (**empty_ratio**, **ver_waste** and **hor_waste**)
and several terms (*empty_ratio*, *ver_waste* and *hor_waste*)
that reflect wasted space are evaluated.
## Packing
## **Packing**
Macro packing is performed as follows \[Algorithm 1 Lines 11-22\]:
- Macros are placed in order of non-increasing macro area.
- All macros are placed, one by one, into the (**n_rows**, **n_cols**) _gridcells_.
- All macros are placed, one by one, into the (*n_rows*, *n_cols*) _gridcells_.
If the current macro cannot be placed, then the _grid_ is infeasible and the next
candidate _grid_ is considered.
- A macro is placed at the **first** (according to row-major order) _gridcell_ where it can be legally placed.
- Placement of a macro means placing that macro's center at the center of some _gridcell_.
- The placement of a macro's center at the center of some _gridcell_ is legal if (1) no part of the macro is outside of the canvas, and (2) no overlap of the macro with any previously-placed macro is induced.
## Metric
After macro packing, we can calculate the **empty_ratio** of current _grid_, i.e.,
the number of empty _gridcells_ over the total number of _gridcells_ (**n_rows*** **n_cols**).
## **Metric**
After macro packing, we can calculate the *empty_ratio* of current _grid_, i.e.,
the number of empty _gridcells_ over the total number of _gridcells_ (*n_rows * n_cols*).
A _gridcell_ is claimed as an empty _gridcell_ if the intersection area of placed macros with it is less than 0.00001 times its area.
Next we calculate the **hor_waste** and **ver_waste** as described in following algorithm.
Next we calculate the *hor_waste* and *ver_waste* as described in following algorithm.
The _grid_ with best **metric** is noted as **n_rows_opt** and **n_cols_opt**.
## **Grid Simplification**
Once we have found *n_rows_opt* and *n_cols_opt* as described above,
we seek a smaller _grid_ that has similar *metric* properties. \[Algorithm 1 Lines 33-39\]
Specifically, we find values of *n_rows_actual* and *n_cols_actual* such that
its *metric* is within some tolerance (5\% in Circuit Training) of the optimal *metric*,
and *n_rows_actual * n_cols_actual* is minimized.
This is the _grid_ that is used in Circuit Training.
To our understanding, the foregoing procedure results in grids that are of similar sizes, e.g., with ~25 <= *n_rows_actual* , *n_cols_actual* <= ~40.
## Grid Simplification
Once we have found **n_rows_opt** and **n_cols_opt** as described above,
we seek a smaller _grid_ that has similar metric properties. \[Algorithm 1 Lines 33-39\]
Specifically, we find values of **n_rows_actual** and **n_cols_actual** such that
its **metric** is within some tolerance (5\% in Circuit Training) of the optimal **metric**,
and **n_rows_actual*** **n_cols_actual** is minimized.
This is the grid that is used in Circuit Training.
To our understanding, the foregoing procedure results in grids that are of similar sizes, e.g., with ~25 <= **n_rows_actual** , **n_cols_actual** <= ~40.
@@ -3,42 +3,46 @@ Grouping is an important preprocessing step of clustering.
The grouping step in Circuit Training requires as inputs:
the post-synthesis gate-level netlist (standard cells and hard macros),
placed IOs (ports, or terminals), typically at the borders of the chip canvas,
the grid of **n_rows** rows and **n_cols** columns of _gridcells_, which defines the gridded layout canvas.
the grid of *n_rows* rows and *n_cols* columns of _gridcells_, which defines the gridded layout canvas.
The purpose of grouping, to our understanding, is to ensure that closely-related standard-cell logic,
which connect to the same macro or the same clump of IO (noted as IO cluster), belong to the same standard-cell clusters.
## **The Grouping Process**
The grouping consists of three steps:
- Group the macro pins of the same macro into a cluster.
In Circuit Training, the netlist consists of four building elements:
standard cells, IO ports, macro pins and macros.
In Circuit Training, the netlist consists of four types of elements:
standard cells, IO ports, macro pins, and macros.
The following figure shows an example of netlist representation in Circuit Training.
The left part is the real netlist; The right part is the Netlist Protocol Buffer
representation in Circuit Training. The solid arrow means the real signal net and the dashed
arrow means the virtual nets between macro A and its macro pins.
We can see that the macro pins and the related macro are both basic elements in the netlist, whereas there is no pins of standard cells. Thus, it's necessary to group the macros pins of the same macro into a cluster, because the macro pins of the same macro will always stay together during macro placement. Note that only the macro pins are grouped and the macro itself is not grouped. For example, in this figure, **D\[0\]**, **D\[1\]**, **D\[2\]**, **Q\[0\]**,
The left part is the real netlist; the right part is the Netlist Protocol Buffer
representation in Circuit Training. The solid arrows indicate the real signal net and the dashed
arrows indicate the virtual nets between macro A and its macro pins.
We can see that the macro pins and the related macro are both basic elements in the netlist, whereas there are no pins of standard cells. Thus, it is necessary to group the macros pins of the same macro into a cluster, because the macro pins of the same macro will always stay together during macro placement. Note that only the macro pins are grouped and the macro itself is not grouped. For example, in this figure, **D\[0\]**, **D\[1\]**, **D\[2\]**, **Q\[0\]**,
**Q\[1\]**, **Q\[2\]** are grouped into **cluster_1**, but **cluster_1** does not include macro A.
- Group the IOs that are within close proximity of each other boundary by boundary,
following the order of **LEFT**<span>→</span>**TOP**<span>→</span>**RIGHT**<span>→</span>**BOTTOM**. For the **LEFT**/**RIGHT**(**TOP**/**Bottom**) boundary, we sort the all the ports on the boundary based on their y (x) coordinates in a non-decreasing order. Starting from the first IO port on the boundary, we group the IO ports within each **grid_height** (**grid_width**) into an IO cluster. For example, in following figure, we have three IO clusters on **TOP** boundary and two IO clusters on **RIGHT** boundary. The **grid_width** and **grid_height** are calculated based on the **n_cols** and **n_rows**:
-**grid_width** = **canvas_width** / **n_cols**
-**grid_height** = **canvas_height** / **n_rows**
</p>
<palign="center">
Figure 1. Illustration of the netlist representation in Circuit Training.
</p>
- Group the IOs that are within close proximity of each other, boundary by boundary,
following the order of **LEFT**<span>→</span>**TOP**<span>→</span>**RIGHT**<span>→</span>**BOTTOM**. For the **LEFT**/**RIGHT**(**TOP**/**Bottom**) boundary, we sort the all the ports on the boundary based on their y (x) coordinates in a non-decreasing order. Starting from the first IO port on the boundary, we group the IO ports within each *grid_height* (*grid_width*) into an IO cluster. For example, in following figure, we have three IO clusters on the **TOP** boundary and two IO clusters on the **RIGHT** boundary. The *grid_width* and *grid_height* are calculated based on the *n_cols* and *n_rows*:
-*grid_width = canvas_width / n_cols*
-*grid_height = canvas_height / n_rows*
<palign="center">
<imgsrc="./images/IO_Groups.png"width="1600"/>
</p>
<palign="center">
Figure 2. Illustration of grouping IO ports.
</p>
- Group the close-related standard cells,
- Group the closely-related standard cells,
which connect to the same macro or the same IO cluster.
Suppose that we have a design with 100 clusters of macro pins (i.e., 100 macros) and 10 clusters of IOs.
Before we grouping the close-related standard cells to these clusters of macro pins or IOs,
we assign each cluster with a cluster id from 0 to 119.
Then for each cluster, we traverse the netlist and assign the same cluster id to the "immediate fanins" and "immediate fanouts" of its element (macro pin or IO).
Suppose that we have a design with 100 clusters of macro pins (i.e., 100 macros) and 20 clusters of IOs.
Before grouping the closely-related standard cells to these clusters of macro pins or IOs,
we assign each cluster a cluster id from 0 to 119.
Then, for each cluster, we traverse the netlist and assign the same cluster id to the "immediate fanins" and "immediate fanouts" of its element (macro pin or IO).
Note that "immediate fanin" is equivalent to "transitive fanins up to level K_in = 1", and that "immediate fanouts" is equivalent to "transitive fanouts up to level K_out = 1".
It is our understanding that both K_in and K_out are always set to a default value of 1
in Circuit Training. However, other values might be applied.
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@@ -55,9 +59,10 @@ Each group is recorded in the “.fix file” that is part of the input to the h
## **How Grouping Scripts Are used**
We provide [(an example)](https://github.com/TILOS-AI-Institute/MacroPlacement/blob/main/CodeElements/Grouping/test/test.py) about the usage of our grouping scripts.
Basically our grouping scripts take follows as inputs: (i) [(setup_file)](https://github.com/TILOS-AI-Institute/MacroPlacement/blob/main/CodeElements/Grouping/test/setup.tcl)
including enablement information (lefs/libs), synthesized gate-level netlist (*.v), def file with placed IOs (*.def); (ii) n_rows and n_cols determined by the [(Gridding)](https://github.com/TILOS-AI-Institute/MacroPlacement/tree/main/CodeElements/Gridding) step; (iii) K_in and K_out parameters; (iv) global_net_threshold for ignoring global nets. If a net has more than global_net_threshold instances, we ignore such net when we search "transitive" fanins and fanouts. After
including enablement information (lefs/libs), synthesized gate-level netlist (*.v), def file with placed IOs (*.def); (ii) n_rows and n_cols determined by the [(Gridding)](https://github.com/TILOS-AI-Institute/MacroPlacement/tree/main/CodeElements/Gridding) step; (iii) K_in and K_out parameters; (iv) global_net_threshold for ignoring global nets. If a net has more than global_net_threshold instances, we ignore such this net when we search "transitive" fanins and fanouts. After
running grouping scripts, you will get the **.fix** file.
# Thanks
We thank Google engineers for Q&A in a shared document, as well as live discussions on May 19, 2022, that explained the grouping method used in Circuit Training. All errors of understanding and implementation are the authors'. We will rectify such errors as soon as possible after being made aware of them.
@@ -15,6 +15,9 @@ We are glad to see [grouping (clustering)](https://github.com/google-research/ci
However, these [grouping (clustering)](https://github.com/google-research/circuit_training/tree/main/circuit_training/grouping) scripts still rely on the wrapper functions of plc client, which is a black box for the community. In this doc, we document the implementation details of gridding, grouping and clustering. We implement all the code elements from scratch using python scripts, and our results match exactly that of Circuit Training.
Note that we build our implementation on top of the [OpenROAD](https://github.com/ravi-varadarajan/OpenROAD.git) application, you need to build your own OpenROAD binary before you can run our scripts. We also provide the [flow.py](https://github.com/TILOS-AI-Institute/MacroPlacement/blob/main/CodeElements/CodeFlowIntegration/flow.py), which runs Gridding, Grouping and Hypergraph Clustering in sequence.
## Table of Content
-[Gridding](#gridding)
...
...
@@ -311,22 +314,20 @@ All methodologies that span synthesis and placement (of which we are aware) must
#### **Our Implementation of Hypergraph Clustering.**
Our implementation of hypergraph clustering takes the synthesized netlist and a .def file with placed IO ports as input,
then generates the clustered netlist (in lef/def format) using hMETIS (1998 binary).
In default mode, our implementation will also run RePlAce in GUI mode automatically to place the clustered netlist.
We implement the entire flow based on [OpenROAD APIs](https://github.com/the-openroad-project).
**Please refer to [the OpenROAD repo](https://github.com/the-openroad-project) for explanation of each Tcl command.**
We have provided the openroad exe in the [utils](https://github.com/TILOS-AI-Institute/MacroPlacement/tree/main/CodeElements/Clustering/src/utils) dir. Please note that [The OpenROAD Project](https://github.com/the-openroad-project) does not
distribute any compiled binaries. While we build our implementation on top of the OpenROAD application, our effort is not associated with the OpenROAD project.
In default mode, our implementation will generate the clustered netlist in protocol buffer format and cooresponding plc file.
We implement the entire flow based on [OpenROAD APIs](https://github.com/ravi-varadarajan/OpenROAD.git).
**Please refer to [the OpenROAD repo](https://github.com/ravi-varadarajan/OpenROAD.git) for explanation of each Tcl command.**
Please note that [The OpenROAD Project](https://github.com/ravi-varadarajan/OpenROAD.git) does not
distribute any compiled binaries. You need to build your own OpenROAD binary before you run our scripts.
Input file: [setup.tcl](https://github.com/TILOS-AI-Institute/MacroPlacement/blob/main/CodeElements/Clustering/test/setup.tcl)(you can follow the example to set up your own design) and [FixFile](https://github.com/TILOS-AI-Institute/MacroPlacement/blob/main/CodeElements/Clustering/test/fix_files_grouping/ariane.fix.old)(This file is generated by our [Grouping](https://github.com/TILOS-AI-Institute/MacroPlacement/tree/main/CodeElements/Grouping) scripts)
Output_files: [clusters.lef](https://github.com/TILOS-AI-Institute/MacroPlacement/blob/main/CodeElements/Clustering/test/results/OpenROAD/clusters.lef) and [clustered_netlist.def](https://github.com/TILOS-AI-Institute/MacroPlacement/blob/main/CodeElements/Clustering/test/results/OpenROAD/clustered_netlist.def) for OpenROAD flows; [cluster.tcl](https://github.com/TILOS-AI-Institute/MacroPlacement/blob/main/CodeElements/Clustering/test/results/Cadence/ariane_cluster_500.tcl) for Cadence flows; [ariane.pb.txt](https://github.com/TILOS-AI-Institute/MacroPlacement/blob/main/CodeElements/Clustering/test/results/Protocol_buffer_format/ariane.pb.txt) for clustered netlist in protocol buffer format.
Output_files: the clustered netlist in protocol buffer format and cooresponding plc file.
Note that the [example](https://github.com/TILOS-AI-Institute/MacroPlacement/tree/main/CodeElements/Clustering/test) that we provide is the Ariane133 design implemented in NanGate45. The netlist and corresponding def file with placed instances are generated by [Genus iSpatial](https://github.com/TILOS-AI-Institute/MacroPlacement/tree/main/Flows/NanGate45/ariane133) flow. Here, the macro placement is automatically done by the Genus and Innovus tools,
Note that the [example](https://github.com/TILOS-AI-Institute/MacroPlacement/tree/main/CodeElements/Clustering/test) that we provide is the ariane design implemented in NanGate45. The netlist and corresponding def file with placed instances are generated by [Genus iSpatial](https://github.com/TILOS-AI-Institute/MacroPlacement/tree/main/Flows/NanGate45/ariane133) flow. Here the macro placement is automatically done by the Genus and Innovus tools,
i.e., according to Flow **(B.1)** above.
## **Thanks**
We thank Google engineers for Q&A in a shared document, as well as live discussions on May 19, 2022, that explained the hypergraph clustering method used in Circuit Training. All errors of understanding and implementation are the authors'. We will rectify such errors as soon as possible after being made aware of them.
