\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.
