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    FPGA 05/13 · RTL Claude — Simulation vs the Oracle

    1

    Clock-by-clock RTL simulation and verification against golden Python oracles with 4-state logic preservation.

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    See it in action

    You say

    Run the RTL simulation for the dsp_macro block against the Python oracle. Ensure the comparison happens after nonblocking assignments and check for pipeline hazards.

    Your agent does

    Simulation trace tb_dsp_macro.trace generated. 4-state logic preserved. Compared 10,000 vectors. Read-after-write hazard test cases passed. Comparison report: bad==0. Pre-synthesis behavioral pass complete. Post-synthesis run scheduled. Blind spots: 2 unsynchronized CDCs flagged for Station 07.

    What you get

    Compare RTL data values tick-by-tick against a golden reference model.Detect pipeline hazards using targeted read-after-write test vectors.Identify regressions by running pre- and post-synthesis simulation passes.Quantify simulation blind spots like CDC and sub-cycle timing issues.

    About this skill

    The problem

    Functional simulations often pass while hardware fails because "no warnings" doesn't mean "correct data." Subtle bugs like off-by-one clock shifts or silent range overflows remain invisible without cycle-accurate value comparison.

    What it does

    • Performs tick-for-tick value comparisons between RTL and a golden Python oracle.
    • Preserves 4-state logic (X/Z) during comparison to prevent unknown states from collapsing into false zeros.
    • Validates designs before and after synthesis using gate-level netlists with realistic delays.
    • Injects targeted hazard vectors to catch read-after-write hazards and pipeline conflicts.
    • Generates a definitive bad==0 report based on trace file analysis.

    Frameworks & tools

    Verilog RTL, Python oracles, Vivado, gate-level simulators, and event-driven simulation environments.

    Why this beats prompting it yourself

    Generic prompts ignore the nuances of event-driven simulation timing. This skill enforces strict comparison points after all nonblocking assignments, ensuring zero simulator-dependent jitter and accurate hazard detection that simple "check my code" prompts miss.

    Use cases

    • Verifying a new RTL block against a golden mathematical model.
    • Catching synthesis-induced regressions in gate-level netlists.
    • Debugging pipeline hazards and memory port conflicts with targeted vectors.
    • Documenting simulation blind spots like CDC and metastability for downstream teams.

    Known limitations

    Cannot detect metastability, sub-cycle glitches, or clock-domain crossing issues, as these are invisible to event-driven simulators.

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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