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/08-future-work.tex

@@ -0,0 +1,40 @@
+\section{Future Work --- 2H}
+\label{sec:future}
+
+The 1H work covered attention end to end. The natural next step is to
+complete the decoder and then to ask how compute and data should be
+distributed for realistic, agentic workloads.
+
+\paragraph{From attention to the full decoder: FFN and MoE.} A decoder
+block is attention followed by a feed-forward network (FFN), and in modern
+models that FFN is increasingly a mixture-of-experts (MoE) layer. The FFN
+is the compute-rich counterpart to attention---it is where the composite
+command's MAC-efficiency gains (\S\ref{sec:gemm}) should matter most---so
+adding it gives a balanced view of a full block instead of its
+memory-bound half alone. MoE adds a new dimension: a routing step selects a
+few experts per token, turning the dense FFN GEMM into a sparse, data-
+dependent dispatch. The open questions are how to issue expert GEMMs as
+composites under data-dependent token counts, and how to move tokens to
+experts efficiently---an all-to-all-shaped communication pattern distinct
+from the all-reduce studied here, and a natural extension of the PE\_IPCQ
+work.
+
+\paragraph{Compute and data distribution for full LLM decoding.} With both
+attention and FFN/MoE in hand, the question becomes where each layer's
+compute and state should live. Attention is KV-bound and favors keeping the
+KV cache close to the PEs that consume it; FFN/MoE is compute-bound and
+favors spreading GEMM work across PEs; MoE routing makes the optimal
+placement token- and time-dependent. A 2H goal is to use KernBench to
+explore these placement and parallelization trade-offs---tensor vs.\ expert
+vs.\ sequence parallelism---under a single, software-stack-independent
+model, so the interconnect and memory implications of each choice are
+measured rather than assumed.
+
+\paragraph{Agentic workloads.} Agentic inference interleaves many
+short, bursty decode requests with tool use and long shared contexts,
+which stresses the system differently from a single long generation:
+context reuse across requests, dynamic batching, and uneven expert load all
+change how compute and data should be dispersed. Characterizing how total
+compute and data movement distribute across the SIP/CUBE/PE hierarchy under
+such workloads---and which of the 1H hardware levers still dominate when the
+workload is this irregular---is the broader 2H agenda.