\section*{Executive Summary}
\addcontentsline{toc}{section}{Executive Summary}

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.

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.

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. Looking ahead, these same mechanisms provide
the basis for optimizing FFN-intensive workloads such as Mixture-of-Experts (MoE)
layers and for developing integrated optimization strategies spanning both attention
and MoE components within next-generation AI models.
