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 \graphicspath{{figures/}}
 
-\title{\textbf{Hardware--Software Codesign for Attention-Centric\\
-LLM Kernels: GEMM, Collective Communication, and\\
-Fused Grouped-Query Attention}}
-\author{KernBench Team}
-\date{2026 First Half (1H)}
+\title{\textbf{Hardware--Software Co-Design for Grouped-Query Attention on AHBM}}
+
+\author{Mukesh Garg \and Jiyoon Lee \and Yangwook Kang}
+
+\date{
+\small
+AGI Computing Lab, System Technology Group\\
+H1 2026
+}
 
 \begin{document}
 \maketitle
diff --git a/docs/report/1H-codesign-paper/sections/00-exec-summary.tex b/docs/report/1H-codesign-paper/sections/00-exec-summary.tex
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@@ -1,39 +1,33 @@
 \section*{Executive Summary}
 \addcontentsline{toc}{section}{Executive Summary}
 
-This report studies how to make attention-centric LLM kernels fast through
-hardware--software codesign, using KernBench, a discrete-event simulator
-that runs kernels at the source level---without a compiler or other
-software-stack dependency---so that algorithm-level optimizations and the
-hardware features that support them can be isolated and measured.
+Grouped-Query Attention (GQA) has largely replaced GPT-3-style
+multi-head attention in modern decoder-only LLMs (e.g., Llama 3 and
+Mistral) due to its reduced memory and bandwidth requirements. Mapping
+GQA efficiently onto AHBM introduces three architectural requirements:
+optimized placement of KV caches and weights to minimize inter-PE
+communication, low-overhead support for unavoidable cross-PE traffic,
+and efficient pipelining of memory accesses and computation within each
+PE.
 
-The 1H work centers on attention. Building on prior multi-head-attention
-work, we target FlashAttention-style Grouped-Query Attention (GQA) and find
-that a fast fused attention kernel rests on two enabling optimizations,
-each studied in its own right and then combined inside the fused kernel:
-a \textbf{composite command} that issues a tiled GEMM as a single,
-self-routing pipeline, and \textbf{PE\_IPCQ}, a per-PE on-device collective
-engine for all-reduce.
+To address these requirements, this report introduces three
+hardware--software co-design mechanisms: GQA-aware placement
+of KV caches and weights across TCM, SRAM, and HBM; PE\_IPCQ,
+an efficient on-device collective communication primitive; and a
+composite-command GEMM pipeline that tightly pipelines memory
+and compute operations within each PE under PE\_SCHEDULER control.
+Kernbench-based evaluation show up to \SI{38}{\percent} lower collective
+communication overhead, \SIrange{25}{35}{\percent} lower all-reduce
+latency than a mesh-based interconnect, and up to \SI{78}{\percent} of
+peak MAC efficiency for compute-intensive GEMM kernels. These results
+demonstrate that the fused GQA kernel can efficiently exploit AHBM's HBM bandwidth.
 
-The measured results form a single, consistent story. For GEMM, the
-composite command lets compute-rich shapes reach
-\textasciitilde\SI{78}{\percent} of peak MAC efficiency, with the simulator's
-measured efficiency tracking the analytic prediction within
-\SIrange{10}{20}{\percent}---both a performance result and a validation of
-the latency model. For all-reduce, PE\_IPCQ delivers on-device collectives
-whose latency tracks the interconnect's physical limits; a 2D-torus
-inter-device fabric is \SI{20}{}--\SI{25}{\percent} faster than a mesh or
-ring at scale, and staging the collective in on-PE TCM rather than HBM or
-SRAM saves a further \SI{14}{}--\SI{38}{\percent}. For fused GQA, the two
-enablers combine: because the composite command keeps the MAC array nearly
-idle, the kernel is exposed as overwhelmingly data-movement bound---the DMA
-engine outweighs the compute engines by two to three orders of magnitude,
-and on-device IPCQ reduction traffic scales with users and devices.
-
-\textbf{Bottom line.} For an attention-dominated decoder, the hardware
-changes worth keeping are the ones that move and reduce data faster: the
-single-command self-routing GEMM pipeline (which makes the MAC array
-usable and exposes the real bottleneck), the on-device PE\_IPCQ collective
-with its compute/communication virtual-channel split, fast on-PE staging
-memory, and wrap-around (torus) inter-device links. Additional raw MAC
-throughput is not the limiting factor for this workload.
+While GQA serves as the motivating workload in this study, the
+resulting architectural mechanisms are broadly applicable across the
+AHBM software stack. PE\_IPCQ provides a scalable communication
+substrate for collective operations and communication-intensive workloads, while
+composite-command execution enables efficient fusion of memory
+movement, GEMM kernels, normalization, and other element-wise
+operations. Together with the hierarchical data-placement framework,
+these capabilities form a reusable foundation for future AI kernels
+and communication libraries on AHBM.