[Docs] Add deep learning benchmark document.

docs/DeepLearningBenchmark.md

+# Deep Learning Benchmark
+
+The deep learning benchmark aims to evaluate models, layers, and operations from different frameworks. 
+Currently, we support model evaluation, and the layers and operations parts are working in progress.
+
+## Models
+
+### MobileNet (IREE -> MLIR Core -> MLIR Toolchain)
+
+This code generation road depends on different frameworks (i.e., IREE and TensorFlow), which are difficult to automate. 
+We thus provide a model source code generated from IREE and perform the preprocessing. 
+The following is the process of generating code.
+
+**1. Build IREE and dependencies**
+
+- Build IREE. [steps](https://google.github.io/iree/building-from-source/getting-started/)
+- Build python bindings and importers. [steps](https://google.github.io/iree/building-from-source/python-bindings-and-importers/)
+- Check the tools: 
+    - `iree/integrations/tensorflow/bazel-bin/iree_tf_compiler/iree-import-tf`
+    - `iree-build/iree/tools/iree-opt`
+
+**2. Prepare the model**
+
+- Download model from TensorFlow Hub. [link](https://hub.tensorflow.google.cn/google/tf2-preview/mobilenet_v2/classification/4)
+- Add signature in mode. [steps](https://google.github.io/iree/ml-frameworks/tensorflow/#missing-serving-signature-in-savedmodel)
+
+**3. Generate mlir file and preprocess**
+
+- Generate mlir file.
+
+```
+<iree-import-tf> -tf-import-type=savedmodel_v1 -tf-savedmodel-exported-names=predict </path/to/mobilenet/> -o <generated file>
+```
+
+- Lower to MLIR core abstraction.
+
+```
+<iree-opt> --iree-util-fold-globals <generated file> -o <output file>
+```
+
+- Preprocess
+
+First of all, erase the `predict` function in the output file. 
+And then, rename the `predict__ireesm` function and remove the attribute of the function. 
+Now you can get the final model file.