💬 Feedback & Feature Requests: Supercharging OpenROAD with an AI-Powered MCP Server 🤖

[luarss]

Hey everyone,

We're exploring the integration of a Model Context Protocol (MCP) server to bring powerful AI-driven capabilities to OpenROAD. The vision is to create an "AI assistant" that can understand our tools, automate complex tasks, and help you achieve better PPA (Power, Performance, and Area) with less effort.

To ensure we build something that is truly useful for the OpenROAD community, we need your expertise and feedback. We want to know where an AI assistant could have the most impact on your day-to-day work.

Our guiding question is:

What are the most time-consuming, repetitive, or challenging parts of your physical design workflow that you wish an AI could simplify or automate?

To help structure the conversation, we've broken this down into a few key areas. Please feel free to add your thoughts on any or all of these topics.

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🛠️ AI for Flow Automation & Tool Orchestration

How can an AI assistant make the OpenROAD flow more intelligent and easier to manage?

• Intelligent Flow Recipes: Would you use an AI that could suggest an optimal flow or a set of tool parameters based on your design's characteristics (e.g., "Give me a good starting recipe for a high-performance, low-power IoT design on SKY130")?
• Automated Debugging: If a run fails, what information would you want an AI to automatically gather and summarize for you? (e.g., "Summarize the critical path timing violations from the last run and point to the relevant lines in the log files").
• Natural Language Commands: What are some of the tcl commands you use most often that you would rather express in plain English? (e.g., "Show me the top 10 worst timing paths" or "What's the current routing congestion?").

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🧠 AI for Design Space Exploration & PPA Optimization

How can we leverage AI to explore the vast design space more effectively and achieve better results?

• PPA Trade-offs: Would you be interested in an AI that could help you explore PPA trade-offs? (e.g., "Show me how the clock frequency impacts power consumption for this block" or "Find the floorplan that gives me the best balance of area and timing").
• "What-if" Scenarios: What kind of "what-if" analyses would be most helpful? (e.g., "What if I add another 100 flip-flops to this module? How would that impact the overall timing and routability?").
• Visual Analysis: How could an AI help you better understand and visualize your design in the OpenROAD GUI? (e.g., "Highlight all the cells on the critical path" or "Show me a heatmap of the areas with the highest routing congestion").

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🔌 AI for Integrating with the Broader EDA Ecosystem

How can an AI assistant help OpenROAD work more seamlessly with other tools and platforms?

• Connecting to Simulators: Could an AI help bridge the gap between OpenROAD and simulation tools? (e.g., "Run a gate-level simulation of the critical path and report the results").
• Version Control Integration: What kind of interactions with Git would be most useful? (e.g., "Compare the PPA results between the current branch and the main branch" or "Find the commit that introduced this timing violation").
• Documentation & Reporting: What kind of reports or documentation would you want an AI to generate automatically? (e.g., "Create a summary of the final PPA results for this design, including key metrics and screenshots of the layout").

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✨ Your "Blue Sky" Ideas for an AI-Powered OpenROAD

If you could give an AI assistant for OpenROAD any superpower, what would it be? No idea is too ambitious!

We are incredibly excited about the potential of AI to democratize hardware design and further the mission of OpenROAD. Your feedback will be invaluable in shaping the future of this project.

Thanks for your contributions!

[oharboe]

Let AI do the waiting, rinsing and repeating of variables

1. Start by reading about configuration variables, such as PLACE_DENSITY
2. I start with some settings for these variables
3. Run a flow that can take minutes, hours, days
4. Read the logs
5. Tweak variables
6. Rince and repeat

What I want AI to do is to read all the logs and create a summary of recommendations, then rinse and repeat. The logs are insufficient though, this requires looking at the design in the GUI, look for placement density, various kinds of problems, PDN, etc. DRC errors, etc. etc.

This is a tricky task that requires deep knowledge of what is going on both in the EDA field as well as OpenROAD specifically.

Note also that there's a lot of oportunity for AI to optimize things here: there are steps that can be skipped and run in parallel to get information. No need to re-run synthesis to tweak a bizillion macro placement variables. AI could also look use macro placement results and correlate better to end result. A field of study of its own...

Also, for most bigger, confidential designs, this would have to happen offline with an air-gap capability.

[oharboe]

In particular, read this table

Types of variables

Variables values are set in ORFS scripts or config.mk files and are kept in source control together with configuration files and RTL.

It is an ongoing effort to move variables upwards in the categories below.

Category	Definition	User Involvement	Examples	Automation Potential	Notes
Trivial	Automatically determined by tool with near-optimal results.	None (unless debugging)	Buffer sizing, default layers	High – can be hidden	Best if invisible; surfaced only in debug or verbose mode.
Easy	Requires input, but easy to tune using reports or visuals.	Moderate – copy/edit from reports	PLACE_DENSITY	Medium–High	Smooth response curves, intuitive tuning.
Complex	Small changes in values may result in large effects.	High – requires multiple runs/sweeps	CTS_DISTANCE_BUF, small changes can have large effects on skew and quality of results. Small changes to independent inputs, such as RTL, can invalidate earlier "good values".	Low–Medium	Needs scripted sweeps and statistical evaluation.