SA260324R-0104_r1

SA260324R-0104

① SA Source

• Source: 開啟完整 SA 文章
• Section: GPU and LPU Integration: Attention FFN Disaggregation (AFD)
• Line hint: 111

Context Before

Attention and FFN are subsets of operations in a model. In a model forward pass, attention’s output feeds into a token router, and the token router assigns each token to k experts, where each expert is an FFN. Attention and FFN have very different performance properties.

Evidence

During decode phase, the GPU utilization of attention barely improves when scaling batch size due to being bounded by loading KV cache.

Context After

In contrast, the GPU utilization of FFN scales with batch size comparatively better.

② Atomic Claim

在 decode 階段，attention 的 GPU utilization 在提高 batch size 時幾乎不會改善，因為受到載入 KV cache 的限制。

• Epistemic Mode: ASSERTED
• Mapping Status: PARTIAL

③ Semantic Frame

{
  "attribute": "ATTENTION_UTILIZATION_SCALING",
  "context_nodes": [
    {
      "id": "04_knowledge_base/Decode",
      "label": "decode"
    },
    {
      "id": "04_knowledge_base/Batch size",
      "label": "batch size"
    },
    {
      "id": "04_knowledge_base/KV cache",
      "label": "KV cache"
    }
  ],
  "entity": {
    "id": "04_knowledge_base/GPU",
    "label": "GPU"
  },
  "frame_type": "ATTRIBUTE",
  "qualifiers": {
    "condition_text": "attention 本身在此 claim 沒有 canonical entity",
    "numeric_mentions": [],
    "temporal_mentions": []
  },
  "value": {
    "numeric_mentions": [],
    "value_text": "在 decode 階段，提高 batch size 時 utilization 幾乎不改善，原因是受到 KV cache 載入限制。"
  }
}

④ Canonical Entity Mapping

Role	Surface Label	Canonical Target
entity	GPU	GPU
context_node	decode	Decode
context_node	batch size	04_knowledge_base/Batch size
context_node	KV cache	04_knowledge_base/KV cache

⑤ 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。