clm_44bf75e74cef48f299cfa76504ec0ace_r2

SA260324R-0116

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: 113

Context Before

Source: SemiAnalysis, MegaScale-Infer

Evidence

With AFD, token routing from GPUs to LPUs can become the bottleneck, especially under strict latency constraints. The token routing flow involves two operations: dispatch and combine. In the dispatch step, we route each token to their top k experts with an All-to-All collective operation. After experts complete their computation, we perform the combine step, where the outputs are sent back to the source location with a reverse All-to-All collective, continuing the next layer’s computation.

Context After

Source: SemiAnalysis

② Atomic Claim

在 AFD 下，從 GPU 到 LPU 的 token routing 可能成為 bottleneck，尤其是在嚴格 latency constraint 下。

• Epistemic Mode: ASSERTED
• Mapping Status: COMPLETE

③ Semantic Frame

{
  "frame_type": "NARY_RELATION",
  "participants": [
    {
      "node": {
        "id": "04_knowledge_base/Attention-FFN Disaggregation (AFD)",
        "label": "AFD"
      },
      "role": "technique"
    },
    {
      "node": {
        "id": "04_knowledge_base/GPU",
        "label": "GPU"
      },
      "role": "source_device"
    },
    {
      "node": {
        "id": "04_knowledge_base/Groq LPU",
        "label": "LPU"
      },
      "role": "destination_device"
    }
  ],
  "qualifiers": {
    "condition_text": "尤其在嚴格 latency constraint 下",
    "numeric_mentions": [],
    "temporal_mentions": []
  },
  "relation_type": "TOKEN_ROUTING_CAN_BECOME_BOTTLENECK"
}

④ Canonical Entity Mapping

Role	Surface Label	Canonical Target
technique	AFD	04_knowledge_base/Attention-FFN Disaggregation (AFD)
source_device	GPU	GPU
destination_device	LPU	04_knowledge_base/Groq LPU

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

在 AFD 下，從 GPU 到 LPU 的 token routing 可能成為 bottleneck，尤其是在嚴格 latency constraint 下。

V2 Semantic Frame Draft

{
  "frame_type": "NARY_RELATION",
  "participants": [
    {
      "node": {
        "id": "04_knowledge_base/Attention-FFN Disaggregation (AFD)",
        "label": "AFD"
      },
      "role": "technique"
    },
    {
      "node": {
        "id": "04_knowledge_base/GPU",
        "label": "GPU"
      },
      "role": "source_device"
    },
    {
      "node": {
        "id": "04_knowledge_base/Groq LPU",
        "label": "LPU"
      },
      "role": "destination_device"
    }
  ],
  "qualifiers": {
    "condition_text": "尤其在嚴格 latency constraint 下",
    "numeric_mentions": [],
    "temporal_mentions": []
  },
  "relation_type": "TOKEN_ROUTING_CAN_BECOME_BOTTLENECK"
}

V2 Revision Metadata Draft

{
  "epistemic_mode": "ASSERTED",
  "mapping_status": "COMPLETE"
}

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。