NIEK2-0108
① SA Source
- Source: 開啟完整 SA 文章
- Section:
GPU and LPU Integration: Attention FFN Disaggregation (AFD) - Line hint:
95
Context Before
Source: SemiAnalysis
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. During decode phase, the GPU utilization of attention barely improves when scaling batch size due to being bounded by loading KV cache. In contrast, the GPU utilization of FFN scales with batch size comparatively better.
Evidence
This is something we have worked with certain hardware vendors and memory companies on with our inference simulator for more than 6 months
Context After
Source: MegaScale-Infer, SemiAnalysis
② Atomic Claim
SemiAnalysis 已與部分硬體供應商與記憶體公司使用其 inference simulator 研究此議題超過六個月。
- Epistemic Mode:
ASSERTED - Mapping Status:
CLAIM_ONLY
③ Semantic Frame
{
"frame_type": "CLAIM_ONLY",
"qualifiers": {
"condition_text": null,
"numeric_mentions": [],
"temporal_mentions": []
},
"reason": "NO_NODE_REQUIRED",
"statement": "SemiAnalysis 已與部分硬體供應商與記憶體公司使用其 inference simulator 研究此議題超過六個月。"
}④ Canonical Entity Mapping
- 此
CLAIM_ONLYrevision 沒有 canonical Entity mapping。
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。