Add measured Snapdragon NPU / GPU section (Galaxy S26)

Numbers from the 2026-08-23 Galaxy S26 sweep. Primary log:
litertlm-convert/portal_work/bench_all.txt + bench_redo.txt, summarised in
portal_work/NOTES.md (2026-08-23, the 50-model NPU/GPU comparison).
Every row carries device, SoC, runtime, N and thermal state. Measurements
are joined to this repo by exact .tflite file name.

README.md

@@ -102,6 +102,19 @@ Measured on a **Pixel 8a** (Tensor G3, Android 16) with the standard TFLite [`be
 
 **The two GPU rows are different runtimes, not a contradiction.** The `LITERT_CL` figure is the one recorded when this model shipped, taken through LiteRT's own `CompiledModel` accelerator — the path the Kotlin sample app and the LiteRT API use. The `TfLiteGpuDelegateV2` figure is the classic TFLite OpenCL delegate, measured with a tool anyone can download and re-run. They agree on how much of the graph the GPU takes; they disagree on speed, and the classic delegate is the slower of the two here. Read the `TfLiteGpuDelegateV2` row as a reproducible floor, not as this model's speed on LiteRT.
 
+## Snapdragon NPU (Hexagon)
+
+The NPU is **2.20x faster** than the GPU (2.96 ms against 6.51 ms) and loads 7.44x faster (113 ms against 840 ms).
+
+| backend | inference (median / min) | load |
+|---|---:|---:|
+| NPU (Hexagon v81) | 2.96 ms / 2.92 ms | 113 ms |
+| GPU (Adreno) | 6.51 ms / 6.22 ms | 840 ms |
+
+Measured on a **Samsung Galaxy S26** (Snapdragon 8 Elite Gen 5 / SM8850, Hexagon v81, Android 16), LiteRT `CompiledModel` 2.2.0, one accelerator per process, 5 warm-up runs then N=50 timed runs, median reported. Every run held thermal status `NONE` throughout. Headroom 0.66-0.67, where 1.0 is the throttling threshold.
+
+The NPU rows here ran artifacts compiled ahead of time for SM8850 with QAIRT 2.47.0; the GPU rows ran the published files as they are. LiteRT can also compile for the NPU on the device at first load, which is what lets you ship the published file unchanged — that path and the ten runtime libraries it needs are in the [NPU recipe](https://github.com/john-rocky/hf-to-litertlm/blob/main/docs/android-npu.md), and we did not measure it here. GPU wiring is in the [GPU recipe](https://github.com/john-rocky/hf-to-litertlm/blob/main/docs/android-gpu.md).
+
 ## License
 
 MIT (Ultra-Fast-Lane-Detection / cfzd). Trained on CULane.