b11574: hexagon: optimize CPY/CONCAT — round 3, short-rows edition (#30341)

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Derived & scientifically synthesized from Llama.cpp Tensor Acceleration.
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Key Architectural Takeaway

hexagon: move the converting CPY in blocks of rows per DMA descriptor so short rows are not DMA-latency bound hex-cpy: cleanup cpy cursor handling hex-cpy-concat: introduce threaded dma/hvx gather-row helper Co-authored-by: Ruslan Karymov [email protected] hex-cpy: remove leftover rejections for extended buffers hex-cpy: relax the gate for fast-path hex-cpy: remove scalar divs and vtcm access and use explicit gather_sync hex-cpy: add proper support for mdev work splitting to fastpath hex-cpy: optimize dense gather-row tasks hex-cpy: unroll gather-rows loops hex-cpy: add fastpath for f16 hex-cpy-concat: support for colapsing 3/4D dims hex-cpy:....

Executive Summary

hexagon: move the converting CPY in blocks of rows per DMA descriptor so short rows are not DMA-latency bound hex-cpy: cleanup cpy cursor handling hex-cpy-concat: introduce threaded dma/hvx gather-row helper Co-authored-by: Ruslan Karymov [email protected] hex-cpy: remove leftover rejections for extended buffers hex-cpy: relax the gate for fast-path hex-cpy: remove scalar divs and vtcm access and use explicit gather_sync hex-cpy: add proper support for mdev work splitting to fastpath hex-cpy: optimize dense gather-row tasks hex-cpy: unroll gather-rows loops hex-cpy: add fastpath for f16 hex-cpy-concat: support for colapsing 3/4D dims hex-cpy:...

Artificial Intelligence Architecture & Model Evaluation

From an artificial intelligence architecture, model weights governance, and inference efficiency perspective: - **Weights Accessibility & Sovereignty:** Evaluates whether weights are open for private self-hosting or locked behind centralized cloud APIs. - **Quantization & Edge Performance:** Kernel optimizations (4-bit/8-bit GGUF, AWQ, EXL2) allow high tokens-per-second on consumer GPUs and Apple Silicon. - **Reasoning & Architectural Scaling:** Scrutinizes mixture-of-experts (MoE), attention mechanisms, and fine-tuning datasets against open community benchmarks.

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