added readme

Author: Karadarya

examples/tutorial_emnist_tflm/README.md

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+EMNIST Convolution Neural Network Example
+========================================================================
+This example shows how to convert EMNIST Tensorflow model into Tensorflow Lite Micro format and use it in embARC MLI application. 
+
+## Requirements
+---------------
+* MetaWare Development Tools
+    * [Order the ARC MetaWare Development Toolkit](https://www.synopsys.com/dw/ipdir.php?ds=sw_metaware)
+    * [Evaluation license](https://eval.synopsys.com/)
+* gmake (pre-installed as a part of MetaWare tools)
+* embARC MLI Library
+    * `git clone https://github.com/foss-for-synopsys-dwc-arc-processors/embarc_mli`
+* Tensorflow Lite for Microcontrollers (part of Tensorflow)
+    * `git clone https://github.com/tensorflow/tensorflow.git`
+* Text Editor
+
+Installation process of the following dependencies is described in [Getting Started](#getting-started) section:
+* Python 3.7
+* virtualenv (optional)
+* Dependencies from requirements.txt
+    * NumPy 1.16.4
+    * Matplotlib
+    * Jupyter Lab / Notebook
+    * tf-nightly 2.3
+    * Keras
+    * emnist 
+    
+## Getting started
+---------------
+
+## Install Python and create a virtual environment
+
+0. It is recommended that you uninstall your previous Python distribution.
+1. Download official [Python 3.7 distribution](https://www.python.org/ftp/python/3.7.4/python-3.7.4-amd64.exe).
+2. Install py launcher and pip. Do not add Python to the PATH. After this, the `py` command in command line is your entry point to Python interpreter.
+3. (optional) Install virtualenv with `py -m pip install --upgrade pip virtualenv`
+4. (optional) Create virtual environment with `py -m virtualenv py_env`.
+5. (optional) Activate virtual environment with `./py_env/Scripts/activate`.
+6. Execute ` cd ./embarc_mli/examples/tutorial_emnist_tflm`.
+
+## Install pip requirements
+```bash
+pip install --upgrade pip setuptools
+pip install -r requirements.txt
+python -c "import emnist; emnist.ensure_cached_data();
+```
+## Generate Tensorflow Lite Micro library
+Open root directory of tensorflow in terminal (use Cygwin or MinGW terminal if you're on Windows). Run:
+```bash
+make -f tensorflow/lite/micro/tools/make/Makefile TARGET_ARCH=arc microlite
+```
+Generated library *libtensorflow-microlite.a* can be found in *<tensorflow-dir>/tensorflow/lite/micro/tools/make/gen/<target>/lib*. Copy it to third_party directory of this example.
+
+## Convert the model
+To convert the model, run the Jupyter Notebook:
+```bash
+jupyter notebook conversion_tutorial/model_conversion.ipynb
+```
+After completing the tutorial you should have model and test samples generated. Copy *conversion_tutorial/generated/model.h* and *conversion_tutorial/generated/test_samples.cc* to *src* folder.
+
+Now you can build and run the example with DesignWare ARC nSIM simulator:
+```bash
+gmake app run 
+```
+