paper: add /paper skill + 1H HW-SW codesign report (GEMM, All-Reduce, fused GQA)

New `/paper` slash-command skill that synthesizes ADR/SPEC content and live
KernBench benchmark results into a sectioned LaTeX technical paper compiled
to PDF with Tectonic (auto-installed). The skill negotiates a TOC, grounds
every number in committed artifacts or fresh bench runs, and keeps
report-only benches isolated.

This commit also includes the first generated report:
- docs/report/1H-codesign-paper/ — main.tex + per-section .tex, figures,
  toc.md contract, and the built 8-page main.pdf. Covers the platform
  (source-level kernels, latency model + accuracy, HW config from
  topology.yaml), GEMM via composite command, All-Reduce via PE_IPCQ, and
  fused GQA combining both, plus discussion/conclusion/2H future work.
- scripts/paper/ — isolated report harnesses (not registered benches):
  paper_gqa_latency.py harvests per-panel GQA end-to-end latency + engine
  occupancy (the milestone only emitted op-counts); paper_plot_gqa.py
  renders the GQA figures.

GEMM/All-Reduce reuse committed milestone figures/CSVs; GQA results are
generated fresh. Honest flags retained: PE_CPU dispatch cost is 0 in this
config, and the proposed two-composite softmax_merge decode is marked
designed-not-measured.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

docs/report/1H-codesign-paper/figures/gqa_latency.json

@@ -0,0 +1,97 @@
+{
+  "version": 1,
+  "panels": [
+    "single_user_prefill_gqa",
+    "multi_user_prefill_gqa",
+    "single_user_decode_gqa",
+    "multi_user_decode_gqa"
+  ],
+  "rows": [
+    {
+      "panel": "single_user_prefill_gqa",
+      "kind": "prefill",
+      "C": 1,
+      "S_kv": 16,
+      "latency_ns": 445.1180000000004,
+      "op_log_summary": {
+        "gemm_count": 2,
+        "ipcq_copy_count": 0,
+        "dma_read_count": 3,
+        "dma_write_count": 1
+      },
+      "engine_occupancy_ns": {
+        "pe_gemm": 2.048000000000002,
+        "pe_math": 5.0,
+        "pe_dma": 72.0,
+        "pe_fetch_store": 0,
+        "pe_ipcq": 0,
+        "pe_cpu": 0
+      }
+    },
+    {
+      "panel": "multi_user_prefill_gqa",
+      "kind": "prefill",
+      "C": 4,
+      "S_kv": 16,
+      "latency_ns": 4630.408000000019,
+      "op_log_summary": {
+        "gemm_count": 32,
+        "ipcq_copy_count": 24,
+        "dma_read_count": 12,
+        "dma_write_count": 4
+      },
+      "engine_occupancy_ns": {
+        "pe_gemm": 8.192000000000917,
+        "pe_math": 236.0,
+        "pe_dma": 1446.945000000003,
+        "pe_fetch_store": 0,
+        "pe_ipcq": 0,
+        "pe_cpu": 0
+      }
+    },
+    {
+      "panel": "single_user_decode_gqa",
+      "kind": "decode",
+      "C": 1,
+      "P": 8,
+      "S_kv": 64,
+      "latency_ns": 3631.730500000015,
+      "op_log_summary": {
+        "gemm_count": 16,
+        "ipcq_copy_count": 21,
+        "dma_read_count": 24,
+        "dma_write_count": 1
+      },
+      "engine_occupancy_ns": {
+        "pe_gemm": 16.383999999998196,
+        "pe_math": 160.0,
+        "pe_dma": 1450.1150000000016,
+        "pe_fetch_store": 0,
+        "pe_ipcq": 0,
+        "pe_cpu": 0
+      }
+    },
+    {
+      "panel": "multi_user_decode_gqa",
+      "kind": "decode",
+      "C": 4,
+      "P": 8,
+      "S_kv": 128,
+      "latency_ns": 6692.578999999866,
+      "op_log_summary": {
+        "gemm_count": 64,
+        "ipcq_copy_count": 93,
+        "dma_read_count": 96,
+        "dma_write_count": 1
+      },
+      "engine_occupancy_ns": {
+        "pe_gemm": 32.76799999998184,
+        "pe_math": 688.0000000000009,
+        "pe_dma": 15919.751500000013,
+        "pe_fetch_store": 0,
+        "pe_ipcq": 0,
+        "pe_cpu": 0
+      }
+    }
+  ]
+}