clm_07766083e6ab4b45a86abdc4310b6355_r1

NIEK2-0101

① SA Source

• Source: 開啟完整 SA 文章
• Section: GPU and LPU Integration: Attention FFN Disaggregation (AFD)
• Line hint: 87

Context Before

Source: Nvidia

Now with an understanding of what LPUs are good for we can understand how they fit into inference setups. NVIDIA introduced LPUs to improve the performance of high interactivity scenarios. In those scenarios, LPUs can leverage their low-latency capabilities to improve the decode phase latencies. One way LPUs can improve decode phase latencies is by applying the Attention FFN Disaggregation (AFD) technique, introduced in MegaScale-Infer ↗ and Step-3 ↗.

Evidence

LPU’s high SRAM bandwidth and low-latency capabilities can help accelerate this iterative process

Context After

Source: SemiAnalysis

② Atomic Claim

LPU 的高 SRAM 頻寬與低延遲能力，有助於加速這種反覆迭代的運算流程。

• Epistemic Mode: ASSERTED
• Mapping Status: COMPLETE

③ Semantic Frame

{
  "attribute": "BANDWIDTH",
  "context_nodes": [
    {
      "id": "04_knowledge_base/SRAM",
      "label": "SRAM"
    }
  ],
  "entity": {
    "id": "04_knowledge_base/Groq LPU",
    "label": "LPU"
  },
  "frame_type": "ATTRIBUTE",
  "qualifiers": {
    "condition_text": null,
    "numeric_mentions": [],
    "temporal_mentions": []
  },
  "value": {
    "numeric_mentions": [],
    "value_text": "LPU 的高 SRAM 頻寬與低延遲能力，有助於加速這種反覆迭代的運算流程。"
  }
}

④ Canonical Entity Mapping

Role	Surface Label	Canonical Target
entity	LPU	04_knowledge_base/Groq LPU
context_0	SRAM	SRAM

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。