{
  "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": "2610.21096",
  "title": "Helix: A Unified Programmable Accelerator for Diverse Krylov Subspace Solvers",
  "summary": "Helix replaces single-solver or reconfiguration-per-solver FPGA designs for Krylov subspace methods with a unified programmable architecture built on shared computational primitives, a DSL compiler, and programmable datapaths. It costs an FPGA prototyping flow (Xilinx Alveo U55C class hardware with HBM), a custom DSL toolchain, and 4.7% LUT / 4.0% FF plus 0.25%-0.57% execution-time overhead for programmability. It can fail for the reader because it addresses sparse linear system solving in scientific computing, not LLM inference or training, and requires FPGA hardware and HLS expertise that a GPU-and-API-constrained ML team does not possess.",
  "rationale": "The work accelerates numerical linear algebra on FPGAs, a workload and hardware platform unrelated to the reader's LLM production decisions on a single 24 GB GPU or third-party APIs.",
  "technique": "Programmable FPGA accelerator for Krylov solvers",
  "family": "outro",
  "recommendation": "nao_aplica",
  "published": "2026-10-15",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.21096",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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      "statement": "Up to 7.91x speedup over Xilinx XcgSolver and 1.16x over the prior FPGA accelerator Callipepla; sustains 47.5%-96.9% of an RTX PRO 6000 GPU's throughput with 2.29x-4.66x higher energy efficiency across eight Krylov solvers.",
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      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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      "dimension": "security",
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      "dimension": "reproducibility",
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  "risks": [],
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  "open_questions": [],
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
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  "equations_status": "not_fetched",
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}