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    FPGA 10/13 · Saleae Claude — Measurement as Proof

    1

    Verifies FPGA hardware behavior by comparing real-world Saleae logic captures against simulation predictions.

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    See it in action

    You say

    Plan a Saleae capture for my SPI accelerator. The driver uses CPOL=0, CPHA=1 at 10MHz. I need to compare the physical MISO/MOSI pins against my sim_trace.vcd and seal the resulting .sal file.

    Your agent does

    I have set the capture plan: 100MS/s sample rate (10x margin), SPI decoder to Mode 1 (CPOL=0, CPHA=1). Comparison target is sim_trace.vcd. After recording, run scripts/sal_seal.py capture_spi_2023-10-27.sal to generate the SHA-256 custody line for HANDOVER_SPI.md.

    What you get

    Validate FPGA timing by comparing physical pins to simulation traces.Configure UART/SPI/I2C decoders based on specific driver code parameters.Generate SHA-256 sealed measurement logs for technical audits.Identify bottlenecks by separating kernel speed from end-to-end latency.

    About this skill

    The problem

    Relying on simulation or internal debug cores creates a feedback loop where systematic errors remain hidden. Without independent external measurement, performance claims are self-reported predictions rather than verified hardware reality.

    What it does

    • Plans hardware captures by calculating required sample rate margins and trigger points against simulation predictions.
    • Configures protocol decoders for UART, SPI, and I2C based on actual driver implementation rather than generic datasheets.
    • Executes bit-for-bit comparisons between physical signal captures and RTL simulation traces to identify regressions.
    • Calculates separate timing metrics for raw kernel execution versus end-to-end system paths to prevent inflated speedup claims.
    • Generates cryptographically sealed capture archives with SHA-256 hashes for chain-of-custody verification.

    Frameworks & tools

    Saleae Logic hardware and software, SPI, I2C, UART protocols, RTL simulation traces, and Python-based sealing scripts.

    Why this beats prompting it yourself

    Generic prompts often miss subtle protocol misconfigurations like SPI clock phase mismatches or insufficient oversampling. This skill enforces a strict measurement-before-prediction workflow and automates the custody hashing required for rigorous hardware audits.

    Use cases

    • Verifying FPGA accelerator throughput against simulated benchmarks.
    • Debugging communication mismatches between driver software and RTL implementations.
    • Auditing hardware performance for official technical reports and client delivery.
    • Disclosing logic overhead caused by internal embedded logic analyzers.

    Known limitations

    Requires physical Saleae logic analyzer hardware and broken-out signal pins or headers for external measurement.

    How to install

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

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