{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A self-hosted model uses more compute or memory than your workload can justify.",
    "change": "Check for a usable pruned checkpoint and runtime support before attempting model surgery.",
    "baseline": "The original unpruned model at the same precision, context, and load.",
    "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": "Task quality, actual GPU memory, serving throughput, and preparation cost.",
    "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": "2610.19665",
  "title": "ESL-former: Event Sparse Linear Transformer for FPGA-Accelerated Malware Detection",
  "summary": "The method replaces explicit quadratic attention and binary-mask sparse execution with a linear transformer whose accumulated key-value state is used to score and prune tokens and channels, reorganized by a Top-K buffer and hardware compaction unit into dense streams for FPGA multiply-accumulate arrays. The cost is a custom FPGA accelerator design, a contribution-aware scoring and compaction pipeline, and dependence on linear attention rather than standard softmax attention. It can fail if the contribution-based pruning discards tokens needed for rare malware variants, and its gains do not transfer to GPU or API-based deployments.",
  "rationale": "The technique targets FPGA edge hardware and a specific malware-detection workload, neither of which the reader possesses or operates, so it offers no actionable production path on a single GPU or third-party APIs.",
  "technique": "Event-sparse linear transformer with Top-K token compaction for FPGA",
  "family": "esparsidade_e_poda",
  "recommendation": "nao_aplica",
  "published": "2026-10-14",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.19665",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
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      "dimension": "compatibility",
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  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
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  "equations_status": "not_fetched",
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}