{
  "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": "2609.29727",
  "title": "VQ-LIC: Shared Vector-Quantized Learned Image Compression on a Resource-Constrained FPGA",
  "summary": "VQ-LIC replaces a conventional learned image compression encoder and separate VQ compute array with an asymmetric edge-cloud codec: a compact INT8 depthwise-pointwise analysis transform plus multi-codebook VQ on the FPGA edge, sharing one reusable DW/PW engine pair, with reconstruction offloaded to a large cloud decoder. The cost is infrastructure mismatch for this reader: it requires FPGA deployment with custom RTL, an edge-cloud split architecture, and accepts a modest PSNR tradeoff (28.69 dB at 0.1398 bpp) relative to larger codecs. It can fail if the target deployment lacks an FPGA edge device or a cloud decoder endpoint, if the RTL latency model does not transfer to other silicon, and if the PSNR degradation is unacceptable for the application's quality requirements.",
  "rationale": "The technique is an FPGA hardware codec for image compression, which is outside the reader's GPU/API-based ML production scope and offers no adoptable component for their stack.",
  "technique": "FPGA vector-quantized learned image codec",
  "family": "outro",
  "recommendation": "nao_aplica",
  "published": "2026-09-24",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.29727",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "VQ-LIC replaces a conventional learned image compression encoder and separate VQ compute array with an asymmetric edge-cloud codec: a compact INT8 depthwise-pointwise analysis transform plus multi-codebook VQ on the FPGA edge, sharing one reusable DW/PW engine pair, with reconstruction offloaded to a large cloud decoder. The cost is infrastructure mismatch for this reader: it requires FPGA deployment with custom RTL, an edge-cloud split architecture, and accepts a modest PSNR tradeoff (28.69 dB at 0.1398 bpp) relative to larger codecs. It can fail if the target deployment lacks an FPGA edge device or a cloud decoder endpoint, if the RTL latency model does not transfer to other silicon, and if the PSNR degradation is unacceptable for the application's quality requirements.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.29727",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Uses an order of magnitude fewer DSPs than comparable FPGA LIC accelerators; post-training codebook reduction cuts VQ arithmetic and codebook storage by 4x; full pipeline runs at 47.98 fps and 42.84 mJ per frame on a Zynq-7020.",
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      "source_url": "https://arxiv.org/abs/2609.29727",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
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      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "compute",
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      "source_url": "",
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    },
    {
      "dimension": "latency",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
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      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    }
  ],
  "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": ""
}