GPU-Based Autorouting for KiCad

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This project began when I decided it would be easier to write an autorouter than route a 8000+ net backplane by hand. This is a KiCad plugin with a few different algorithms, the coolest of which is a 'Manhattan routing grid' autorouter that routes along orthogonal traces. The basic idea was to steal an algorithm from FPGA routing and apply it to PCBs. I'm using CuPy for speeding up the routing; CPU-bound is at least 10x slower than the GPU version. This is in a very pre-alpha state, but it does _technically_ work. It's not great by any measure but then again it is an autorouter. I have a writeup with the how and why it was made: https://bbenchoff.github.io/pages/OrthoRoute.html And a video showing it route a 512-net backplane in just over 2 minutes: https://www.youtube.com/watch?v=KXxxNQPTagA This is very cool and one of the first good uses of the KiCad IPC API that was released a few months ago. If this sounds interesting and useful, PRs and issues welcome.

Visit product View on Hacker NewsOct 29, 2025

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: using · Missing: mac, agents, macos

50%50% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, 000, io · Missing: https docs, excited, just released

49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: video, month · Missing: mobile apps, ios, personal

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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