feat(edgeai): Switch from Neo-AI-DLR to TVM-RT

* With [0], the ti-tidl-osrt has now dropped Neo-AI-DLR [1]
& switched to TVM-RT. Hence, update the documentation
to highlight the same.

[0] - https://git.ti.com/cgit/edgeai/meta-edgeai/commit/recipes-tisdk/ti-tidl/ti-tidl-osrt.bb?h=scarthgap&id=1b88fbdf717da6f0c3ac1a6d7d4b23d2dff5f63d

[1] - https://github.com/neo-ai/neo-ai-dlr

Signed-off-by: Chirag Shilwant <c-shilwant@ti.com>

Author: cshilwant

source/devices/AM62AX/edgeai/software_components_version.rst

@@ -23,7 +23,7 @@ List of software components used in this version
 +------------------------------+---------------------+
 | Meson                        | 1.3.1               |
 +------------------------------+---------------------+
-| NeoAI - DLR                  | 1.13.0              |
+| TVM-RT                       | 0.18.0_TIDL         |
 +------------------------------+---------------------+
 | Tensorflow                   | 2.12_TIDL           |
 +------------------------------+---------------------+

source/edgeai/inference_models.rst

@@ -165,7 +165,7 @@ Model Artifacts and Deployments
 
 The SDK makes use of pre-compiled DNN (Deep Neural Network) models and performs
 inference using various OSRT (open source runtime) such as TFLite runtime,
-ONNX runtime and Neo AI-DLR (related to TVM).
+ONNX runtime and TVM-RT.
 
 Model deployment can be performed in several ways --
 Please view the :doc:`Sample Apps page <./sample_apps>` for more details on

source/edgeai/sdk_components.rst

@@ -35,7 +35,7 @@ build and install steps please refer to **edgeai-app-stack** on `GitHub <https:/
 edgeai-gst-apps
 ---------------
 These are plug-and-play deep learning applications which support running open
-source runtime frameworks such as TFLite, ONNX and NeoAI-DLR with a variety of
+source runtime frameworks such as TFLite, ONNX and TVM-RT with a variety of
 input and output configurations.
 
 The latest source code with fixes can be pulled from `TI Edge AI GStreamer apps <https://github.com/TexasInstruments/edgeai-gst-apps>`_

source/edgeai/sdk_overview.rst

@@ -36,7 +36,7 @@ using a live camera and display.
 
 The SDK also enables an interplay of multiple open-source components such as
 GStreamer, OpenVx, OpenCV and deep learning runtime such as TFLite, ONNX and
-Neo-AI DLR. The reference applications showcase perception based examples such
+TVM-RT. The reference applications showcase perception based examples such
 as image classification, object detection and semantic segmentation in both
 Python and C++ variants. The SDK supports edit-build-debug cycles directly on
 the target and also on PC to cross compile and build the applications.