#11197: MobilenetV2 bring-up in WH150 with new conv-api

models/experimental/functional_mobilenetv2/README.md

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+# MobilenetV2
+The MobileNetV2 model is a convolutional neural network (CNN) architecture designed for efficient mobile and embedded vision applications. It was introduced in the paper ["MobileNetV2: Inverted Residuals and Linear Bottlenecks"](https://arxiv.org/abs/1801.04381). </br>
+The MobileNetV2 model has been pre-trained on the ImageNet dataset and can be used for various tasks such as image classification, object detection, and semantic segmentation. It has achieved state-of-the-art performance on several benchmarks 1 for mobile and embedded vision applications.
+
+## How to Run
+
+To run the demo, make sure to build the project, activate the environment, and set the appropriate environment variables.
+For more information, refer [installation and build guide](https://docs.tenstorrent.com/tt-metalium/latest/get_started/get_started.html#install-and-build).
+
+To run the functional Mobilenetv2 model on a single-chip:
+```sh
+pytest --disable-warnings models/experimental/functional_mobilenetv2/test/test_ttnn_mobilenetv2.py
+```
+
+## Supported Hardware
+- N150
+
+## Other Details
+
+- Inputs by default are random data.
+- The model weights will be automatically downloaded from Google Drive using wget implemented in weights_download.sh.