{
  "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": "2610.21313",
  "title": "PSA: A Pruning-Friendly Sparse-Aware Accelerator for Efficient Sparse Convolution in SNNs",
  "summary": "PSA replaces dense systolic-array execution of spiking neural network convolutions with a custom accelerator combining sparse-aware merge-accumulation, group-wise sparse im2col lowered to on-chip SpMM, and pruning-aware gating. It costs a dedicated hardware design (RTL-level dataflow, index alignment, and gating logic) and applies only to SNN inference on specialized silicon, not to GPU or API-based deployment. It can fail to deliver its reported gains on workloads with low spike sparsity or without neuron pruning, and the results are demonstrated only on a small benchmark (ResNet-18/CIFAR-10), so generality is unestablished.",
  "rationale": "The technique is a custom hardware accelerator for spiking neural networks, which the reader cannot fabricate or deploy given a single GPU or third-party APIs and no SNN production workload is implied.",
  "technique": "Sparse-aware SNN hardware accelerator (PSA)",
  "family": "esparsidade_e_poda",
  "recommendation": "nao_aplica",
  "published": "2026-10-15",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.21313",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 0,
  "citations": null,
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