report(1H): add Agentic Workloads (§6) and HW-Spec Search (§7) sections

- §6 Supporting Agentic Workloads: how the fused GQA design extends to
  agentic fan-out/fan-in; three-layer split (framework/runtime/kernel);
  Stationary-KV vs Distributed-Q execution policies.
- §7 Hardware Performance-Spec Search for GQA: WIP stub (sweep intent
  over GEMM TFLOPS, MATH-engine ALUs, CUBE↔CUBE and SIP↔SIP BW).
- Renumber Discussion/Conclusion/Future-Work to 08/09/10; update
  main.tex input order and toc.md.
- Add Agentic_Runtime_Architecture.md design note; rebuild main.pdf.

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

docs/report/1H-codesign-paper/sections/08-discussion.tex

@@ -0,0 +1,54 @@
+\section{Discussion: which hardware changes are meaningful}
+\label{sec:discussion}
+
+Read together, the three studies point to a consistent ranking of where
+hardware investment pays off for attention-centric decoding.
+
+\paragraph{The composite command is foundational, but indirectly.} Its
+direct effect---driving compute-rich GEMMs to
+\textasciitilde\SI{78}{\percent} of peak---matters most for the
+compute-bound parts of a model (the large feed-forward and projection
+matrices). For attention itself, its more important effect is
+\emph{diagnostic}: by making issue and compute nearly free, it removes the
+overhead that would otherwise mask the true bottleneck, and the fused GQA
+results then show unambiguously that the kernel is data-movement bound.
+Without a cheap, self-routing issue mechanism we would not be able to tell
+whether attention is slow because of compute or because of data movement;
+with it, the answer is clear.
+
+\paragraph{The communication path is where attention latency actually
+lives.} Every all-reduce and fused-GQA measurement says the same thing:
+the limiting resource is moving and reducing data, not multiplying it. That
+makes the PE\_IPCQ design and the choices around it the highest-value
+hardware levers for this workload:
+\begin{itemize}
+  \item \textbf{On-device collectives with a compute/communication
+  virtual-channel split.} Performing the reduction on the device, with
+  \texttt{vc\_comm} separated from \texttt{vc\_compute} so the reduction
+  does not stall the compute DMA, is what lets attention overlap KV
+  movement with score computation at all.
+  \item \textbf{Fast on-PE staging memory.} Keeping the IPCQ buffer in TCM
+  rather than HBM or SRAM is worth \SI{14}{}--\SI{38}{\percent} of
+  collective latency at large payloads---a pure placement decision with a
+  first-order effect.
+  \item \textbf{Wrap-around (torus) inter-device links.} A torus fabric
+  buys \SI{20}{}--\SI{25}{\percent} over a mesh or ring at scale by
+  shortening the worst-case reduction path.
+\end{itemize}
+
+\paragraph{Raw MAC throughput is not the constraint for attention.} The
+GQA panels leave the GEMM and vector-math engines two to three orders of
+magnitude below the DMA engine in busy time. Adding MAC area would not move
+decode latency; the workload cannot use it. This is the single most
+actionable finding for an attention-dominated roadmap. The implication is
+that future hardware investment should prioritize communication and
+memory-system efficiency over additional compute throughput for
+attention-dominated inference workloads.
+
+\paragraph{Caveats.} These conclusions are achievable-kernel results from a
+deterministic model, not E2E measurements; absolute numbers carry the
+model's idealizations (\S\ref{sec:latency}), though the relative rankings
+that drive the recommendations are robust to them. The headline GQA panels
+are also at modest scale (up to four users, single SIP), and one designed
+refinement---the two-composite \textsf{softmax\_merge} decode---is not yet
+in the measured path. Larger-scale and multi-SIP headline runs are 2H work.