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Exercise 13
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‎.gitignore‎

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.bender
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tmp
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tmp
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plots/
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__pycache__/

‎Makefile‎

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###########
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CLANG_FORMAT_EXECUTABLE ?= clang-format
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## Automatically format the code using clang-format and black
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format:
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@echo -e "\033[1m-> Formatting Python Code...\033[0m"
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@black */*.py

‎ex13/01a_plot_area_bar.py‎

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# Copyright 2025 ETH Zurich and University of Bologna.
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# Licensed under the Apache License, Version 2.0, see LICENSE for details.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Philip Wiese <[email protected]>
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# ------------------------------------------------------------------------------
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# STUDENT TASK 1a:
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#
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# In this exercise, you will visualize the area breakdown of the i_croc module
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# using a bar plot. You will learn common tasks such as scaling axes, setting
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# titles, and customizing the appearance of the plot.
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#
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# The code below extracts the hierarchical area data. Your task is to visualize
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# the area breakdown of the i_croc module as a bar plot and customize the plot
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# to improve its readability.
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#
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# Follow the instructions in the code comments to complete the exercise.
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# ------------------------------------------------------------------------------
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# Import the modules to extract hierarchical area data and plot the results
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from area import extract_areas, get_path
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# Argparse is a module that allows you to parse command-line arguments
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# See https://docs.python.org/3/library/argparse.html for more information
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import argparse
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# Import the matplotlib library for plotting
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# See https://matplotlib.org/stable/gallery/index.html for examples
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from matplotlib import pyplot as plt
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# Import the os module to create directories
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import os
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# IHP-130 sg13g2_stdcell library (1 GE = 7.25 um^2)
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# See http://eda.ee.ethz.ch/index.php?title=Ihp13#What_is_one_gate_equivalent_?
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AREA_PER_GE = 7.25
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if __name__ == "__main__":
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# Usage: python 01a_plot_area_bar.py <area_report_file>
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parser = argparse.ArgumentParser(
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description="Extract hierarchical areas from an OpenROAD area report."
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)
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parser.add_argument("file", type=str, help="Path to the area report file")
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args = parser.parse_args()
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# Extract the hierarchical area data
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area_tree = extract_areas(args.file)
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if area_tree is None:
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print("No hierarchical area data found.")
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exit(-1)
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# Extract the hierarchical area data
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node_i_croc = get_path(area_tree, ["<top>", "i_croc_soc", "i_croc"])
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# Extract names and convert areas to kGE
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names = []
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areas_kge = []
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for node in node_i_croc.children:
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print(node)
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# Convert area from um^2 to kGE
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area_kge = node.area / (AREA_PER_GE * 1e3)
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areas_kge.append(area_kge)
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names.append(node.name)
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# ==========================================================================
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# STUDENT TASKS - MODIFY THE CODE BELOW TO COMPLETE THE EXERCISE
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# ==========================================================================
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# --------------------------------------------------------------------------
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# STUDENT TASK 1a.1: Create a bar plot
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# --------------------------------------------------------------------------
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# Use `plt.bar(...)` to create a bar chart.
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#
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# - The x-axis should represent the component names.
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# - The y-axis should represent the area values.
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# --------------------------------------------------------------------------
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# --------------------------------------------------------------------------
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# STUDENT TASK 1a.2: Improve the readability of the plot
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# --------------------------------------------------------------------------
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# Improve the visualization by:
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# - Rotating x-axis labels if they overlap
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# - Adding grid lines for better readability
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# - Use distinct colors for each bar
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#
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# HINT: Use `plt.xticks(...)` for better readability.
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# You can also use `plt.yscale('log')` if areas vary significantly.
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# --------------------------------------------------------------------------
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# --------------------------------------------------------------------------
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# STUDENT TASK 1a.3: Remap the names of the components
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# --------------------------------------------------------------------------
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# Remap the names of the components for better readability.
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# Remember to move the plot commands to the correct position in the code.
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#
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# HINT: Use the `names` list to remap the names of the components.
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# --------------------------------------------------------------------------
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# --------------------------------------------------------------------------
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# STUDENT TASK 1a.4: Save and display the plot
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# --------------------------------------------------------------------------
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# Save the figure as a high-quality PDF and display the plot.
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#
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# HINT: Use `plt.tight_layout()` to adjust the layout for better
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# readability.
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# Save the figure using `plt.savefig(...)
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# --------------------------------------------------------------------------

‎ex13/01a_plot_area_bar_sol.py‎

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# Copyright 2025 ETH Zurich and University of Bologna.
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# Licensed under the Apache License, Version 2.0, see LICENSE for details.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Philip Wiese <[email protected]>
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# ------------------------------------------------------------------------------
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# STUDENT TASK 1a:
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#
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# In this exercise, you will visualize the area breakdown of the i_croc module
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# using a bar plot. You will learn common tasks such as scaling axes, setting
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# titles, and customizing the appearance of the plot.
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#
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# The code below extracts the hierarchical area data. Your task is to visualize
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# the area breakdown of the i_croc module as a bar plot and customize the plot
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# to improve its readability.
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#
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# Follow the instructions in the code comments to complete the exercise.
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# ------------------------------------------------------------------------------
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# Import the modules to extract hierarchical area data and plot the results
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from area import extract_areas, get_path
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# Argparse is a module that allows you to parse command-line arguments
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# See https://docs.python.org/3/library/argparse.html for more information
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import argparse
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# Import the matplotlib library for plotting
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# See https://matplotlib.org/stable/gallery/index.html for examples
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from matplotlib import pyplot as plt
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# Import the os module to create directories
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import os
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# IHP-130 sg13g2_stdcell library (1 GE = 7.25 um^2)
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# See http://eda.ee.ethz.ch/index.php?title=Ihp13#What_is_one_gate_equivalent_?
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AREA_PER_GE = 7.25
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if __name__ == "__main__":
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# Usage: python 01a_plot_area_bar.py <area_report_file>
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parser = argparse.ArgumentParser(
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description="Extract hierarchical areas from an OpenROAD area report."
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)
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parser.add_argument("file", type=str, help="Path to the area report file")
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args = parser.parse_args()
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# Extract the hierarchical area data
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area_tree = extract_areas(args.file)
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if area_tree is None:
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print("No hierarchical area data found.")
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exit(-1)
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# Extract the hierarchical area data
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node_i_croc = get_path(area_tree, ["<top>", "i_croc_soc", "i_croc"])
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# Extract names and convert areas to kGE
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names = []
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areas_kge = []
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for node in node_i_croc.children:
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print(node)
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# Convert area from um^2 to kGE
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area_kge = node.area / (AREA_PER_GE * 1e3)
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areas_kge.append(area_kge)
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names.append(node.name)
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# ==========================================================================
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# STUDENT TASKS - MODIFY THE CODE BELOW TO COMPLETE THE EXERCISE
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# ==========================================================================
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# --------------------------------------------------------------------------
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# STUDENT TASK 1a.1: Create a bar plot
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# --------------------------------------------------------------------------
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# Use `plt.bar(...)` to create a bar chart.
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#
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# - The x-axis should represent the component names.
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# - The y-axis should represent the area values.
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# --------------------------------------------------------------------------
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# Set the figure size
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plt.figure(figsize=(10, 5))
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# Label for y-axis
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plt.ylabel("Area [kGE]")
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# Label for x-axis
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plt.xlabel("Component")
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# Title
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plt.title("Area Breakdown: i_croc", fontsize=12, fontweight="bold")
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# --------------------------------------------------------------------------
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# STUDENT TASK 1a.2: Improve the readability of the plot
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# --------------------------------------------------------------------------
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# Improve the visualization by:
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# - Rotating x-axis labels if they overlap
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# - Adding grid lines for better readability
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# - Use distinct colors for each bar
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#
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# HINT: Use `plt.xticks(...)` for better readability.
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# You can also use `plt.yscale('log')` if areas vary significantly.
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# --------------------------------------------------------------------------
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# Rotate x-axis labels for better readability
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plt.xticks(rotation=45, ha="right")
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# Add grid lines for better readability
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plt.grid(axis="y", linestyle="--", alpha=0.6)
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# Use distinct colors for each bar
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cmap = plt.get_cmap("tab10")
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colors = [cmap(i) for i in range(len(names))]
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# --------------------------------------------------------------------------
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# STUDENT TASK 1a.3: Remap the names of the components
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# --------------------------------------------------------------------------
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# Remap the names of the components for better readability.
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# Remember to move the plot commands to the correct position in the code.
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#
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# HINT: Use the `names` list to remap the names of the components.
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# --------------------------------------------------------------------------
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# Remap the names of the components for better readability
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names = [
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"SRAM Bank 1",
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"SRAM Bank 2",
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"Core",
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"Debug Module",
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"JTAG TAP",
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"GPIO",
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"SoC Control",
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"Timer",
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"UART",
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]
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# --------------------------------------------------------------------------
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# STUDENT TASK 1a.4: Save and display the plot
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# --------------------------------------------------------------------------
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# Save the figure as a high-quality PDF and display the plot.
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#
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# HINT: Use `plt.tight_layout()` to adjust the layout for better
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# readability.
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# Save the figure using `plt.savefig(...)
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# --------------------------------------------------------------------------
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# Create a bar chart
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plt.bar(names, areas_kge, color=colors)
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# Adjust layout for better
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plt.tight_layout()
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# Create a directory if it does not exist
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os.makedirs("plots", exist_ok=True)
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# Save figure
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plt.savefig("plots/01a_area_breakdown_bar.pdf")
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plt.savefig("plots/01a_area_breakdown_bar.png")
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# Display the plot
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plt.show()

‎ex13/01b_plot_area_pie.py‎

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# Copyright 2025 ETH Zurich and University of Bologna.
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# Licensed under the Apache License, Version 2.0, see LICENSE for details.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Philip Wiese <[email protected]>
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# ------------------------------------------------------------------------------
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# STUDENT TASK 1b:
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#
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# In this exercise, you will visualize the area breakdown of the i_croc module
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# using a pie chart. The area data is extracted from an OpenROAD area report.
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#
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# The code below extracts the hierarchical area data and visualizes it as a
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# bar plot. Your task is to modify the code to generate a pie chart instead.
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#
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# Follow the instructions in the code comments to complete the exercise.
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# ------------------------------------------------------------------------------
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# Import the modules to extract hierarchical area data and plot the results
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from area import extract_areas, get_path
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# Argparse is a module that allows you to parse command-line arguments
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# See https://docs.python.org/3/library/argparse.html for more information
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import argparse
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# Import the matplotlib library for plotting
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# See https://matplotlib.org/stable/gallery/index.html for examples
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from matplotlib import pyplot as plt
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# Import the os module to create directories
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import os
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if __name__ == "__main__":
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# Usage: python 01b_plot_area_pie_sol.py <area_report_file>
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parser = argparse.ArgumentParser(
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description="Extract hierarchical areas from an OpenROAD area report."
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)
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parser.add_argument("file", type=str, help="Path to the area report file")
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args = parser.parse_args()
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# Extract the hierarchical area data
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area_tree = extract_areas(args.file)
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if area_tree is None:
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print("No hierarchical area data found.")
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exit(-1)
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# Extract the hierarchical area data
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node_i_croc = get_path(area_tree, ["<top>", "i_croc_soc", "i_croc"])
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# Extract names and areas
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names = []
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areas = []
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for node in node_i_croc.children:
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print(node)
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names.append(node.name)
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areas.append(node.area)
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# ==========================================================================
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# STUDENT TASKS - MODIFY THE CODE BELOW TO COMPLETE THE EXERCISE
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# ==========================================================================
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# --------------------------------------------------------------------------
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# STUDENT TASK 1b.1: Create a pie chart instead of a bar plot
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# --------------------------------------------------------------------------
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# Instead of a bar plot, generate a pie chart that visualizes the area
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# distribution.
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# Use `plt.pie()` instead of `plt.bar()`.
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#
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# HINT: The `plt.pie()` function requires:
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# - The area values
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# --------------------------------------------------------------------------
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# --------------------------------------------------------------------------
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# STUDENT TASK 1b.2: Improve the readability of the pie chart
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# --------------------------------------------------------------------------
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# Matplotlib provides a colormap function `get_cmap()` that can generate a
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# set of visually distinguishable colors. Alternatively, you can define your
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# own custom colors. Use the colors to visualize the area distribution in
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# the pie chart.
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#
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# Feel free to experiment with other features to improve the readability of
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# the pie chart.
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#
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# HINT: Look at `matplotlib.cm.get_cmap()`
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# Documentation: https://matplotlib.org/stable/api/cm_api.html#matplotlib.cm.get_cmap
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#
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# HINT: The `plt.pie()` function can be suplied with:
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# - Labels (the names of the components)
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# - Colors (from Task 1b.1)
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# - Optional: Use `autopct='%1.1f%%'` to show percentages on the chart
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# --------------------------------------------------------------------------

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