S15.4, CPU half: XFeat runs in ~400 ms per frame on the tablet

tools/onnx-probe-on-device.sh cross-builds dr-segment's onnx_probe
without the embedded segmentation model, pushes it with a model to the
attached device and times two runs. The 768×1024 XFeat export takes
~400 ms on the reference tablet's NEON cores against ~300 ms on the
desktop, with identical output ranges — inside NFR-MRG-1's 1 s per frame.
The blend half of S15.4 waits for a chunked blend to exist.

docs/panorama.md

@@ -183,9 +183,14 @@ standard: `Conv`, `InstanceNormalization`, `AveragePool`, `Resize`, `Slice`,
 the 2.8 MB file through the app's own `ort`-over-tract backend with nothing
 unsupported, in 28 ms, and runs it in **~300 ms on the reference desktop's
 CPU**. The weights ship as `models/keypoints/xfeat-1024.onnx`, recorded in
-`models/LICENCE.md`. Still to do: the tablet figure (S15.4), and a
-keypoint-level comparison against the PyTorch reference once the Rust decoder
-exists — the probe proves the graph runs, not that the numbers match.
+`models/LICENCE.md`. **On the tablet** (S15.4's CPU half, same day):
+`tools/onnx-probe-on-device.sh` cross-builds the probe, and the same file
+runs in **~400 ms per frame** on the reference tablet's NEON cores (ROD2-W09,
+SM8635), with output ranges identical to the desktop's — inside NFR-MRG-1's
+1 s per frame with room to spare, and 1.3× the desktop rather than the 2×
+faces.md §9 measured for its scan. Still to do: a keypoint-level comparison
+against the PyTorch reference once the Rust decoder exists — the probe
+proves the graph runs, not that the numbers match.
 
 The outputs are three maps at 1/8 resolution, 96×128 for the export size:
 64-channel descriptors, 65-channel keypoint logits (each 8×8 cell's position