The bundled CDI descriptors assume the Qualcomm Linux BSP filesystem layout
(/usr/lib/rfsa/adsp). On the equally-official Ubuntu image for RB3 Gen 2 /
Rubik Pi 3, the QAIRT apt packages board-select the Hexagon skels into
/usr/lib/dsp/cdsp and leave /usr/lib/rfsa/adsp as self-referential symlink
loops, so install_cdi silently dropped the skel mounts and NPU offload failed
silently at runtime (or a host-symlink workaround broke on reboot with an
ELOOP CDI mount error).
install_cdi.py now resolves each missing/broken mount from a fallback library
dir and rewrites it to the real file, injects ADSP_LIBRARY_PATH so the DSP
loader finds the skels, and warns loudly (with an optional --strict) when a
critical QNN/HTP library cannot be found instead of failing silently. Docs
updated to cover both images and a stronger verify step.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Add a community-supported board build for Qualcomm SoCs with a Hexagon
NPU, accelerating TFLite object detection through the QNN TFLite delegate
on the Hexagon Tensor Processor (HTP).
Supported boards:
- IQ9100 (IQ-9075 EVK)
- QCS6490 (RB3 Gen 2 Vision Kit / Rubik Pi 3)
- detector: new `qualcomm_tfl` plugin loading libQnnTFLiteDelegate.so on
the HTP backend, reusing the shared TFLite delegate helpers
- docker: `docker/qualcomm` board build (Dockerfile, qualcomm.hcl,
qualcomm.mk) producing the arm64 `-qualcomm` image
- cdi: per-board Container Device Interface descriptors and an install
helper exposing the NPU device nodes and QNN libraries to the container
- ci/codeowners: register the qualcomm build target and code owner
- docs: installation, hardware, and object detector documentation, using
the shared model config dropdown component
- i18n: add the Qualcomm detector label and description to the generated
en config locale
Signed-off-by: Rami Mouro <rmouro@qti.qualcomm.com>
