SA260324R-0113

回到這篇 SA Coverage

① SA Source

  • Source: 開啟完整 SA 文章
  • Section: GPU and LPU Integration: Attention FFN Disaggregation (AFD)
  • Line hint: 107

Context Before

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Source: SemiAnalysis

Evidence

Comparing the two operations, we see attention is stateful due to dynamic KV cache loading patterns, whereas FFN is stateless since the computation only depends on the token inputs. Thus, we disaggregate the computation of attention and FFN. We map attention computations to GPUs, which handle dynamic workloads well. For FFNs, we map them to LPUs, since LPU architecture is inherently deterministic and benefits from static compute workloads.

Context After

image

Source: SemiAnalysis, MegaScale-Infer

② Atomic Claim

The described disaggregated-decode design separates attention and FFN computation.

  • Epistemic Mode: INFERRED
  • Mapping Status: PARTIAL

③ Semantic Frame

{
  "frame_type": "CLAIM_ONLY",
  "qualifiers": {
    "condition_text": null,
    "numeric_mentions": [],
    "temporal_mentions": []
  },
  "reason": "ATTENTION_HAS_NO_CANONICAL_ENTITY_IN_THIS_CLAIM",
  "statement": "The described disaggregated-decode design separates attention and FFN computation."
}

④ Canonical Entity Mapping

  • 此 CLAIM_ONLY revision 沒有 canonical Entity mapping。

⑤ V2 Draft(需要修正時直接編輯)

Inline repair

只編輯以下三個 Draft 區塊。完成後將 review_action 設為 REQUEST_REPAIR,再手動執行 Repair Preview。
執行腳本前,Draft 不會寫入 Claim Store 或 Graph。

V2 Atomic Claim Draft

The described disaggregated-decode design separates attention and FFN computation.

V2 Semantic Frame Draft

{
  "frame_type": "CLAIM_ONLY",
  "qualifiers": {
    "condition_text": null,
    "numeric_mentions": [],
    "temporal_mentions": []
  },
  "reason": "ATTENTION_HAS_NO_CANONICAL_ENTITY_IN_THIS_CLAIM",
  "statement": "The described disaggregated-decode design separates attention and FFN computation."
}

V2 Revision Metadata Draft

{
  "epistemic_mode": "INFERRED",
  "mapping_status": "PARTIAL"
}

⑥ Human Review

請在 Properties 逐項確認:

  • 原文 → Atomic Claim 是否忠實
  • Atomic Claim → Semantic Frame 是否忠實
  • Canonical Entity mapping 是否正確
  • Epistemic mode 是否保留原文語氣
  • 最後選擇 review_action

Review state

Markdown 內文不是正式 approval。只有 Apply bridge 寫入的 Decision Ledger event 才是正式決策。