paper: §2 platform deep-edit + §1 GQA framing + figure regen

§2 (KernBench Platform):
- Fix Table 2 HBM aggregate BW (1024 → 2048 GB/s); drop stale
  hbm_total_bw_gbs from topology.yaml (never read by sim_engine)
- Split PE_CPU / PE_SCHED fixed-cost row; disambiguate from the
  40-cycle command-dispatch FIXED term
- §2.2 two-pass: expand to describe Pass 1 timing and Pass 2 data
  data-correctness path
- §2.3 dispatch model: add motivation sentence for the
  descriptor-size linear form
- §2.4 Accuracy: reorder GEMM → All-reduce → Probe →
  Simplifications; drop FSIM aside (already covered by §4 Fig 5
  caption); soften 'every ns' → 'every modeled latency
  contribution'
- §2.5 HW config: add 64 TFLOP/s vs 2048 GB/s (~31 FLOP/byte)
  balance-point intuition and forward pointer to §5 GQA decode
- Fig 1 caption: separate illustrative topology from experimental
  configuration

§1: tighten GQA-as-primary-bandwidth-bottleneck framing.

Figures: regenerate SIP / CUBE architecture (SVG sources + PDF +
generator scripts).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

docs/report/1H-codesign-paper/sections/01-introduction.tex

@@ -25,9 +25,12 @@ serves as the common evaluation platform for all mechanisms and kernels
 discussed in this study.
 
 This report focuses on Grouped-Query Attention (GQA), one of the most
-performance- and bandwidth-critical components of LLM inference. Modern
-decoder-only models such as Llama~3 and Mistral have largely transitioned
-from GPT-3-style multi-head attention (MHA) to GQA, in which multiple
+performance- and bandwidth-critical components of LLM inference. GQA
+dominates inference-time memory traffic and KV-cache capacity in modern
+LLM serving, making it the primary bandwidth bottleneck on memory-centric
+architectures such as AHBM. Modern decoder-only models such as Llama~3
+and Mistral have largely transitioned from GPT-3-style multi-head
+attention (MHA) to GQA, in which multiple
 query heads share a single KV head to reduce KV cache capacity and
 memory-bandwidth requirements. While GQA improves system efficiency at
 the model level, mapping it efficiently onto AHBM introduces three