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Penguin-rtl – Verilog equivalence checking locally in the browser

Hacker News

Penguin-rtl – Verilog equivalence checking locally in the browser

Hi HN! I made this little web app for formal equivalence checking of Verilog that runs totally locally in the browser. I initially wrote it for myself to (confidently) rewrite clumps of combinational logic. The idea is that the same logic can be implemented in many ways, but not all will map optimally to FPGA resources. This is also something I wish I had during my digital design class, so I'd be thrilled if a student found some use of it. It’s powered by YoWASP, a WASM package of Yosys. Yosys is an open-source logic synthesis tool with extensive Verilog-2005 support and built-in formal verification capabilities. Yosys also has some interesting work in verification for designs with sequential elements, which I’ll be exploring next. I’m also looking into SystemVerilog/VHDL support using plugins. Happy verifying!

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: using, open · Missing: mac, agents, macos
62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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