SA260324R-0115
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
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
Source: SemiAnalysis, MegaScale-Infer
② Atomic Claim
FFN 會被分配到 LPU，因為 LPU 架構本質上是 deterministic，並能受益於 static compute workload。
- Epistemic Mode:
ASSERTED - Mapping Status:
COMPLETE
③ Semantic Frame
{
"additional_nodes": [],
"frame_type": "RELATION",
"object": {
"id": "04_knowledge_base/Groq LPU",
"label": "LPU"
},
"predicate": "MAPS_TO",
"qualifiers": {
"condition_text": "LPU 架構本質上 deterministic，並受益於 static compute workload",
"numeric_mentions": [],
"temporal_mentions": []
},
"subject": {
"id": "04_knowledge_base/Feed-forward Network",
"label": "FFN"
}
}④ Canonical Entity Mapping
| Role | Surface Label | Canonical Target |
|---|---|---|
| subject | FFN | 04_knowledge_base/Feed-forward Network |
| object | 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
FFN 會被分配到 LPU，因為 LPU 架構本質上是 deterministic，並能受益於 static compute workload。
V2 Semantic Frame Draft
{
"additional_nodes": [],
"frame_type": "RELATION",
"object": {
"id": "04_knowledge_base/Groq LPU",
"label": "LPU"
},
"predicate": "MAPS_TO",
"qualifiers": {
"condition_text": "LPU 架構本質上 deterministic，並受益於 static compute workload",
"numeric_mentions": [],
"temporal_mentions": []
},
"subject": {
"id": "04_knowledge_base/Feed-forward Network",
"label": "FFN"
}
}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。