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/sections/07-conclusion.tex

@@ -0,0 +1,27 @@
+\section{Conclusion}
+\label{sec:conclusion}
+
+This 1H work set out to make attention-centric LLM kernels fast through
+hardware--software codesign, and to do so on a platform that isolates
+algorithm-level behavior from the rest of the software stack. The result is
+a coherent picture rather than three separate optimizations. A composite
+command that issues a tiled GEMM as one self-routing pipeline makes the MAC
+array usable---reaching \textasciitilde\SI{78}{\percent} of peak on
+compute-rich shapes with measured efficiency tracking theory---and, just as
+importantly, makes compute cheap enough that the real bottleneck becomes
+visible. A per-PE on-device collective engine, PE\_IPCQ, turns all-reduce
+into a primitive whose latency follows the interconnect's physical limits,
+with topology and staging-memory choices each worth tens of percent. Fused
+Grouped-Query Attention then combines the two and shows the payoff and the
+lesson at once: the kernel is data-movement bound, so the optimizations
+that move and reduce data---not those that add arithmetic---are what
+determine its speed.
+
+The practical conclusion for the hardware roadmap is therefore specific.
+The changes worth keeping are the single-command self-routing GEMM
+pipeline, the on-device PE\_IPCQ collective with its compute/communication
+virtual-channel split, fast on-PE staging memory, and wrap-around
+inter-device links. Additional MAC throughput is not, for this workload, a
+meaningful investment. KernBench made these conclusions measurable by
+holding everything except the algorithm and the hardware fixed; the next
+half extends the same method beyond attention to the rest of the decoder.