{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Start with one repeated product task that has a measurable failure or cost.",
    "change": "Read the methods and identify one change you can isolate in that workflow.",
    "baseline": "Your current implementation on the same held-out tasks.",
    "steps": [
      "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.",
      "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.",
      "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.",
      "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test."
    ],
    "measure": "Successful outcomes, response time, human rework, and total cost per successful task.",
    "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation."
  },
  "builder_review": null,
  "arxiv_id": "2609.18792",
  "title": "Quantifying the Effect of HCLs on a Fixed-Microarchitecture MXFP4 Accelerator",
  "summary": "The paper compares hardware construction languages (Chisel, SpinalHDL, Amaranth, Clash, Bluespec, HLS) against a SystemVerilog baseline for generating an MXFP4 dot-product pipeline on an Artix-7 FPGA, so it replaces hand-written RTL with HCL-generated RTL. It costs nothing in quality of results according to the authors—all variants meet timing at 100 MHz and match or undercut the baseline area—but adopting it requires FPGA design work, an ASIC/RTL toolchain, and hardware engineering expertise. The main failure modes relevant to its own finding are backend-dependent arithmetic lowering and a single width choice silently toggling DSP inference, neither of which concerns an ML engineer deploying on a GPU or via APIs.",
  "rationale": "This is an FPGA/RTL design-methodology study with no actionable technique for production ML inference on a single GPU or third-party APIs.",
  "technique": "Fixed-microarchitecture comparison of hardware construction languages",
  "family": "outro",
  "recommendation": "nao_aplica",
  "published": "2026-09-16",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.18792",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 1,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The paper compares hardware construction languages (Chisel, SpinalHDL, Amaranth, Clash, Bluespec, HLS) against a SystemVerilog baseline for generating an MXFP4 dot-product pipeline on an Artix-7 FPGA, so it replaces hand-written RTL with HCL-generated RTL. It costs nothing in quality of results according to the authors—all variants meet timing at 100 MHz and match or undercut the baseline area—but adopting it requires FPGA design work, an ASIC/RTL toolchain, and hardware engineering expertise. The main failure modes relevant to its own finding are backend-dependent arithmetic lowering and a single width choice silently toggling DSP inference, neither of which concerns an ML engineer deploying on a GPU or via APIs.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.18792",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
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    },
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    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}