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/01-introduction.tex

@@ -0,0 +1,49 @@
+\section{Introduction}
+\label{sec:intro}
+
+The performance of a large language model on an accelerator is decided less
+by peak FLOPS than by how well the kernel exploits the memory system and
+the interconnect. This is especially true of attention, which in the
+decode phase reads a large KV cache to do a small amount of arithmetic.
+Optimizing such kernels is fundamentally a \emph{codesign} problem: the
+algorithm (how to tile, fuse, and reduce) and the hardware (what issue
+mechanism, what collective engine, what memory hierarchy) have to be
+designed against each other. Optimizing only the software leaves the
+hardware idle; adding hardware that the software cannot reach is wasted
+area.
+
+\paragraph{The 1H focus: attention.} This half's work targets attention.
+Multi-head attention (MHA) was studied previously and is taken here as the
+established baseline. The new work is FlashAttention-style tiling and
+Grouped-Query Attention (GQA)---the form used by modern long-context
+decoders, in which several query heads share a single KV head to shrink the
+KV cache. Realizing a fast \emph{fused} GQA kernel, however, is not a
+single optimization. It decomposes into two enabling optimizations that we
+studied separately and then brought together inside the fused kernel:
+
+\begin{enumerate}
+  \item \textbf{GEMM optimization} via a composite command
+  (\S\ref{sec:gemm}): a way to issue a tiled matrix multiply as one
+  self-routing pipeline so the MAC array stays fed without per-tile command
+  overhead. Attention's $Q\!\cdot\!K^{\top}$ and $P\!\cdot\!V$ products are
+  exactly such GEMMs.
+  \item \textbf{Communication optimization} via PE\_IPCQ
+  (\S\ref{sec:allreduce}): a per-PE on-device collective engine that
+  performs all-reduce overlapped with compute. Attention's multi-user and
+  sequence-parallel KV reductions are exactly such collectives.
+\end{enumerate}
+
+The report therefore reads as \emph{two enablers building to one capstone}:
+GEMM (\S\ref{sec:gemm}) and all-reduce (\S\ref{sec:allreduce}) are
+developed and measured on their own, and then fused GQA
+(\S\ref{sec:gqa}) shows them operating together. Before the results,
+\S\ref{sec:platform} describes the KernBench platform---why a
+software-stack-independent, source-level simulator is the right tool for
+this question, how it executes a kernel, how it computes latency and how
+accurate that is, and the exact hardware configuration used throughout.
+We close with a cross-cutting discussion of which hardware changes are
+worth their cost (\S\ref{sec:discussion}), a conclusion
+(\S\ref{sec:conclusion}), and the 2H agenda
+(\S\ref{sec:future}): extending from attention to the feed-forward and
+mixture-of-experts layers, and to the compute/data distribution questions
+that full LLM decoding and agentic workloads raise.