Rosenblatt: project scaffold for RK3588 NPU on mainline

Codename: Frank Rosenblatt — Mark I Perceptron 1958, the first
hardware neural network.  This project lights up the RK3588 NPU on
mainline Linux so the OSS world finally owns the silicon-side of
inference on that chip.

Phase-1 scope: small LLM running CPU + NPU mix on boltzmann (Rock 5
ITX+).  Backend: llama.cpp with a new rknpu ggml backend offloading
INT8 GEMM (attention + FFN matmuls) to the NPU's tile-MAC array while
leaving dequant / RoPE / softmax / sampling / embedding on A76 NEON.

Target model: qwen2.5-1.5B-instruct Q4_K_M GGUF.

Scaffold layout: README.md (frame + 9+1-phase plan), TODO.md (rolling
punch-list), docs/{npu-mainline-status,architecture}.md, kernel/ for
DT bindings + driver tweaks, userspace/{npu-probe,llm-runtime}/,
fleet/boltzmann.yaml.

Next: Phase-1 substrate audit — fill the TBDs in docs/npu-mainline-status.md
with the actual state of Tomeu Vizoso's rknpu / DRM-accel work on
the boltzmann-running kernel.

userspace/llm-runtime/README.md

@@ -0,0 +1,32 @@
+# llm-runtime
+
+llama.cpp fork (or out-of-tree backend) with the rknpu ggml backend.
+
+Code lands here starting at Phase 5 (Plan) — too early in Phase 1.
+
+Until then, this directory holds:
+- design notes (`docs/architecture.md` from project root is authoritative)
+- the eventual `ggml-rknpu/` backend source
+- patch series for upstream submission if quality reaches that bar
+
+## Approach
+
+Two paths to consider in Phase 5:
+
+1. **Fork llama.cpp, add backend in tree.** Easier to keep in sync;
+   harder to upstream because llama.cpp may not want a Rockchip-specific
+   backend that depends on a still-WIP mainline driver.
+2. **Out-of-tree backend, load via llama.cpp's plugin API
+   (`-DGGML_BACKEND_DL=ON`).** Cleaner separation; tracks llama.cpp
+   upstream without our diff being in the way. Recommended unless we
+   need to patch core llama.cpp logic.
+
+Decision deferred to Phase 5.
+
+## Model
+
+Phase-1 target: `qwen2.5-1.5b-instruct-q4_k_m.gguf`. Source:
+hf.co/Qwen/Qwen2.5-1.5B-Instruct-GGUF or built locally with
+llama-quantize.
+
+Stretch: qwen2.5-3B (if memory + NPU SRAM allow), gemma3-2B.