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David Neural Hardware (DNH) is a design environment for turning frozen AI models into specialized inference-hardware candidates. It generates matched programmable and fixed-weight implementations, verifies them against the same model outputs, and makes the tradeoffs in latency, resource use, timing, and flexibility concrete before a team commits to an accelerator design.