uarchlabs

Expertise+AI=Production RTL

High-performance processors with open source RTL and AI co-design methodology.

The first core, Pacino, is an 8-issue out-of-order RVA23S64 RISC-V, designed for competitive SPEC performance.

~/pacino snapshot
// inside pacino

pacino pipeline (simplified)

written up on the blog not yet
full frontend diagrams ↗

Swipe to see the full pipeline

    // recent news
    // ai co-design lessons

    Some interesting lessons learned so far, with links to the relevant posts and IA tasks.

    Don't let the agent write its own expected values.In BP-033, tests derived from the IA's own RTL hid a bug. Pinning the values to the spec first caught it.
    Prove a test can fail before trusting that it passes.Break the design, check that the test catches it, then restore it.
    The planning document can be the defect.An error in the TAGE CTR rules propagated into the RTL and the tests.
    A proof and its reference can share the same error.Anchor checks to something written independently, even for the obvious.
    Some tool limits have no workaround.
    // projects
    pacino

    Out-of-order RVA23S64 RISC-V processor: an 8-issue OOO design targeting competitive SPECint2006 performance. RTL, design decisions, and AI co-design methodology published in the open.

    activeRISC-VRVA23S64 out-of-orderSystemVerilogopen source
    ticfinder

    Finds recurring LLM prose constructions in markdown or plain text, so a human can decide what to cut. Used on every uarchlabs blog post.

    parallel projectPythonspaCy writing toolsopen source
    cachegen

    Cache RTL and C++ model generator. The released example is a 1 MB 4-way cache with tree-PLRU, directed tests and a functional model.

    parallel projectVerilogC++ cache generatoropen source
    // about

    What is uarchlabs?

    uarchlabs is a research-first open source lab exploring how large language models can accelerate microarchitecture design. We share synthesizable RTL alongside the AI-assisted methodology that produced it: prompts, evaluation loops, and design rationale included. Why we started.

    Who is it for?

    Chip architects, hardware researchers, and curious engineers who want to understand both the what and the how of AI-driven hardware design. Whether you want to study the methods, extend the RTL, or contribute, start with the docs.

    Open RTL

    Every design is released as synthesizable RTL under a permissive license. Inspect it, fork it, tape it out.

    AI co-design methods

    We document the full AI-assisted workflow, not just the output.

    Community first

    Issues, discussions, and design decisions happen in public on GitHub.

    // follow