NIEK2-0110
① SA Source
- Source: 開啟完整 SA 文章
- Section:
GPU and LPU Integration: Attention FFN Disaggregation (AFD) - Line hint:
101
Context Before
Source: MegaScale-Infer, SemiAnalysis
Evidence
As a result, each expert receives fewer tokens, leading to lower utilization
Context After
Source: SemiAnalysis
② Atomic Claim
關於 AFD：因此，每個 expert 平均接收到的 token 會減少，導致利用率下降。
- Epistemic Mode:
ASSERTED - Mapping Status:
COMPLETE
③ Semantic Frame
{
"attribute": "UTILIZATION",
"context_nodes": [],
"entity": {
"id": "04_knowledge_base/Attention-FFN Disaggregation (AFD)",
"label": "AFD"
},
"frame_type": "ATTRIBUTE",
"qualifiers": {
"condition_text": null,
"numeric_mentions": [],
"temporal_mentions": []
},
"value": {
"numeric_mentions": [],
"value_text": "關於 AFD：因此，每個 expert 平均接收到的 token 會減少，導致利用率下降。"
}
}④ Canonical Entity Mapping
| Role | Surface Label | Canonical Target |
|---|---|---|
| entity | AFD | 04_knowledge_base/Attention-FFN Disaggregation (AFD) |
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。