boards/beaglebone/ai-64/edge_ai_apps/configuration_file.rst

@@ -230,7 +230,7 @@ Below are some of the use case specific properties:
    output
 
 The content of the model directory and its structure is discussed in detail in
-:ref:`pub_edgeai_import_custom_models`
+:ref:`ai_64_edgeai_import_custom_models`
 
 
 Outputs

boards/beaglebone/ai-64/edge_ai_apps/data_flows.rst

@@ -12,7 +12,7 @@ The **app_edgeai** application at a high level can be split into 3 parts,
 
 Here are the data flows for each reference demo and the corresponding GStreamer
 launch strings that **app_edgeai** application generates. User can interact with
-the application via the :ref:`pub_edgeai_configuration`
+the application via the :ref:`ai_64_edgeai_configuration`
 
 .. _ai_64_edgeai_image_classification_data_flow:
 

boards/beaglebone/ai-64/edge_ai_apps/docker_environment.rst

@@ -76,7 +76,7 @@ access camera, display and other hardware accelerators the SoC has to offer.
 
     After building and running the docker container, one needs to run 
     ``setup_script.sh`` before running any of the demo applications.
-    Please refer to :ref:`pub_edgeai_install_dependencies` for more details.
+    Please refer to :ref:`ai_64_edgeai_install_dependencies` for more details.
 
 .. _ai_64_edgeai_docker_additional_commands:
 
@@ -202,14 +202,14 @@ current location is the desired location then exit this procedure.
 .. code-block:: json
 
   {
-    "graph": "/run/media/nvme0n1/docker_root",
+    "graph": "/run/images/nvme0n1/docker_root",
     "storage-driver": "overlay",
     "live-restore": true
   }
 
 In the configuration above, the key/value pair
-**'"graph": "/run/media/nvme0n1/docker_root"'** defines the root location
-**'/run/media/nvme0n1/docker_root'.**
+**'"graph": "/run/images/nvme0n1/docker_root"'** defines the root location
+**'/run/images/nvme0n1/docker_root'.**
 
 4. Once the daemon.json file has been copied and updated, run the following
    commands

boards/beaglebone/ai-64/edge_ai_apps/getting_started.rst

@@ -60,7 +60,7 @@ USB Camera
 UVC (USB video class) compliant USB cameras are supported on the BeagleBone® AI-64.
 The driver for the same is enabled in linux image. The linux image has been tested with
 C270/C920/C922 versions of Logitech USB cameras. Please refer to
-:ref:`pub_edgeai_multiple_usb_cams` to stream from multiple USB cameras
+`the TI Edge AI SDK FAQ <https://software-dl.ti.com/jacinto7/esd/edgeai-sdk-j721e/latest/exports/docs/faq.html>`__ to stream from multiple USB cameras
 simultaneously.
 
 .. _ai_64_edgeai_imx219_sensor:
@@ -84,7 +84,7 @@ Note that the headers have to be lifted up to connect the cameras
 .. note:: To be updated
     By default IMX219 is disabled. After connecting the camera you can enable it
     by specifying the dtb overlay file in
-    ``/run/media/mmcblk0p1/uenv.txt`` as below,
+    ``/run/images/mmcblk0p1/uenv.txt`` as below,
 
     ``name_overlays=k3-j721e-edgeai-apps.dtbo k3-j721e-sk-rpi-cam-imx219.dtbo``
 
@@ -92,7 +92,7 @@ Note that the headers have to be lifted up to connect the cameras
 
 Two RPi cameras can be connected to 2 headers for multi camera use-cases
 
-Please refer :ref:`pub_edgeai_camera_sources` to know how to list all the cameras
+Please refer :ref:`ai_64_edgeai_camera_sources` to know how to list all the cameras
 connected and select which one to use for the demo.
 
 By default imx219 will be configured to capture at 8 bit, but it also supports
@@ -119,6 +119,7 @@ required:
     - Set the input format in the ``/opt/edge_ai_apps/configs/rpiV2_cam_example.yaml``
       as ``rggb10``
 
+.. _ai_64_edgeai_install_dependencies:
 
 Software setup
 ==============
@@ -131,8 +132,10 @@ Preparing SD card image
 Download the ``bullseye-xfce-edgeai-arm64`` image from the links below and
 flash it to SD card using `Balena etcher <https://www.balena.io/etcher/>`_ tool.
 
-- To use via SD card: `bbai64-debian-11.4-xfce-edgeai-arm64-2022-08-02-10gb.img.xz <https://rcn-ee.net/rootfs/bb.org/testing/2022-08-02/bullseye-xfce-edgeai-arm64/bbai64-debian-11.4-xfce-edgeai-arm64-2022-08-02-10gb.img.xz>`_
-- To flash on eMMC: `bbai64-emmc-flasher-debian-11.4-xfce-edgeai-arm64-2022-08-02-10gb.img.xz <https://rcn-ee.net/rootfs/bb.org/testing/2022-08-02/bullseye-xfce-edgeai-arm64/bbai64-emmc-flasher-debian-11.4-xfce-edgeai-arm64-2022-08-02-10gb.img.xz>`_
+.. todo: Images should be on https://files.debian.cc and linked from https://www.beagleboard.org/distros
+
+- To use via SD card: `bbai64-debian-11.7-xfce-edgeai-arm64-2023-08-05-10gb.img.xz <https://rcn-ee.com/rootfs/release/2023-08-05/bullseye-xfce-edgeai-arm64/bbai64-debian-11.7-xfce-edgeai-arm64-2023-08-05-10gb.img.xz>`_
+- To flash on eMMC: `bbai64-emmc-flasher-debian-11.7-xfce-edgeai-arm64-2023-08-05-10gb.img.xz <https://rcn-ee.com/rootfs/release/2023-08-05/bullseye-xfce-edgeai-arm64/bbai64-emmc-flasher-debian-11.7-xfce-edgeai-arm64-2023-08-05-10gb.img.xz>`_
 
 
 The Balena etcher tool can be installed either on Windows/Linux. Just download the

boards/beaglebone/ai-64/edge_ai_apps/index.rst

@@ -3,6 +3,8 @@
 Edge AI
 ========
 
+.. todo:: Update to latest text from Texas Instruments Edge AI docs
+
 .. toctree::
    :maxdepth: 1
 

boards/beaglebone/ai-64/edge_ai_apps/inference_models.rst

@@ -69,10 +69,10 @@ Each DNN must have the following 3 components:
 
 #. **model**: This directory contains the DNN being targeted to infer
 #. **artifacts**: This directory contains the artifacts generated after the
-   compilation of DNN for SDK, and described in :ref:`pub_edgeai_compile_artifacts`
+   compilation of DNN for SDK, and described in :ref:`ai_64_edgeai_compile_artifacts`
 #. **param.yaml**: A configuration file in yaml format to provide basic
    information about DNN, and associated pre and post processing parameters.
-   More details can be find :ref:`pub_edgeai_params`
+   More details can be find :ref:`ai_64_edgeai_params`
 
 .. _ai_64_edgeai_params:
 

boards/beaglebone/ai-64/edge_ai_apps/performance_visualizer.rst

@@ -6,7 +6,7 @@ Performance Visualization Tool
 The performance visualization tool can be used to view all the performance
 statistics recorded when running the edge AI C++ demo application. This includes
 the CPU and HWA loading, DDR bandwidth, Junction Temperatures and FPS obtained.
-Refer to :ref:`pub_edgeai_available_statistics` for details on the performance 
+Refer to :ref:`ai_64_edgeai_available_statistics` for details on the performance 
 metrics available to be plotted.
 
 This tool works as follows:
@@ -20,7 +20,7 @@ This tool works as follows:
       which can be easily viewed by a visiting a URL in any browser. This script uses
       Streamlit package to update the graphs in real-time, as the Edge AI application
       runs in parallel. However, since Streamlit is not supported in the SDK out of box,
-      this script needs to run on docker. Please refer to :ref:`pub_edgeai_docker_env`
+      this script needs to run on docker. Please refer to :ref:`ai_64_edgeai_docker_env`
       for building and running a docker container.
 
 
@@ -100,4 +100,4 @@ the above image:
 
 For the first three metrics, there is a choice to view line graphs with a 30s
 history or bar graphs with only the real-time values. The remaining eleven have
-real-time bar graphs as the only option.
\ No newline at end of file
+real-time bar graphs as the only option.

boards/beaglebone/ai-64/edge_ai_apps/running_advance_demos.rst

@@ -9,7 +9,7 @@ file.
 
 From a repo of input sources, output sources and models one can define advance
 dataflows which connect them in various configurations. Details on configuration
-file parameters can be found in :ref:`pub_edgeai_configuration`
+file parameters can be found in :ref:`ai_64_edgeai_configuration`
 
 Single input multi inference demo
 ---------------------------------

boards/beaglebone/ai-64/edge_ai_apps/running_simple_demos.rst

@@ -20,7 +20,7 @@ Running Python based demo applications
 Python based demos are simple executable scripts written for image
 classification, object detection and semantic segmentation. Demos are
 configured using a YAML file. Details on configuration file parameters can
-be found in :ref:`pub_edgeai_configuration`
+be found in :ref:`ai_64_edgeai_configuration`
 
 Sample configuration files for out of the box demos can be found in
 ``edge_ai_apps/configs`` this folder also contains a template config file

boards/beaglebone/ai-64/edge_ai_apps/sdk_components.rst

@@ -7,7 +7,7 @@ The BeagleBone® AI-64 Linux for Edge AI can be divided into 3 parts, Applicatio
 BeagleBone® AI-64 Linux and Processor SDK RTOS. Users can get the latest application
 updates and bug fixes from the public repositories (GitHub and git.ti.com)
 which aligns with the SDK releases done quarterly. One can also build every component
-from source by following the steps here, :ref:`pub_edgeai_sdk_development_flow`
+from source by following the steps in the `TI Edge AI SDK development flow <https://software-dl.ti.com/jacinto7/esd/edgeai-sdk-j721e/latest/exports/docs/development_flow.html>`__.
 
 .. figure:: ./images/edgeai-sdk-components.png
    :scale: 50
@@ -74,7 +74,7 @@ edgeai-gst-plugins
 This repo provides the source of custom GStreamer plugins which helps offload
 tasks to TDA4 hardware accelerators and advanced DSPs with the help of
 edgeai-tiovx-modules. The repo gets downloaded, built and installed as part
-of the :ref:`pub_edgeai_install_dependencies` step.
+of the :ref:`ai_64_edgeai_install_dependencies` step.
 
 Source code and documentation: https://github.com/TexasInstruments/edgeai-gst-plugins
 
@@ -85,7 +85,7 @@ edgeai-tiovx-modules
 This repo provides OpenVx modules which help access underlying hardware
 accelerators in the  TDA4 SoC and serves as a bridge between GStreamer
 custom elements and underlying OpenVx custom kernels. The repo gets downloaded,
-built and installed as part of the :ref:`pub_edgeai_install_dependencies` step.
+built and installed as part of the :ref:`ai_64_edgeai_install_dependencies` step.
 
 Source code and documentation: https://github.com/TexasInstruments/edgeai-tiovx-modules
 

boards/beaglebone/ai-64/edge_ai_apps/test_report.rst

@@ -228,4 +228,4 @@ Multi Input Multi Output
 
 .. note::
     * Video file from RTSP server used for RTSP input test
-    * Please refer to the :ref:`pub_edgeai_known_issues` section for more details
+    * Please refer to the `TI Edge AI SDK release notes and known issues <https://software-dl.ti.com/jacinto7/esd/edgeai-sdk-j721e/latest/exports/docs/release_notes.html>`__ for more details

boards/beaglebone/ai-64/images/OSHW_mark_US002120.svg

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