Update to Version 1.0.0

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diff --git a/README.md b/README.md
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-# Extension package of TVM Deep Learning Complier for Renesas DRP-AI accelerators powered by EdgeCortix MERA&trade;
-
-[TVM Documentation](https://tvm.apache.org/docs) |
-[TVM Community](https://tvm.apache.org/community) |
-[TVM github](https://github.com/apache/tvm) |
-
-
-DRP-AI TVM[^1] is Machine Learning Compiler plugin for [Apache TVM](https://github.com/apache/tvm/) provided by Renesas Electronics Corporation.
-
-## License
-(C) Copyright EdgeCortix, Inc. 2022   
-(C) Copyright Renesas Electronics Corporation 2022   
-Contributors Licensed under an Apache-2.0 license.   
-
-## Supported Embedded Platforms
-- Renesas RZ/V2M Evaluation Board Kit
-
-## Introduction
-### Overview   
-This compiler stack is an extension of the DRP-AI Translator to the TVM backend. CPU and DRP-AI can work together for the inference processing of the AI models.  
-
-<img src=./img/tool_stack.png width=350>   
-
-
-### File Configuration
-| Directory | Details |
-|:---|:---|
-|tutorials |Sample compile script|
-|apps |Sample inference application on the target board|
-|setup | Setup scripts for building a TVM environment |
-|obj |Pre-build runtime binaries|
-|tvm | TVM repository from github |
-|3rd party | 3rd party tools |   
-|how-to |Sample to solve specific problems, i.e., How to run validation between x86 and DRP-AI|
-
-
-## Installation
-### Requirements
-Required software is listed below.  
-
-- Ubuntu 18.04  
-- Python 3.6  
-- git
-- [DRP-AI Translator v1.60](#drp-ai-translator)  
-- [RZ/V2M Linux Package v1.2.0](#rzv-software)  
-- [RZ/V2M DRP-AI Support Package v6.00](#rzv-software)
-
-##### DRP-AI Translator
-Download the DRP-AI Translator v1.60 from the Software section in [RZ/V2M Software](https://www.renesas.com/rzv2m)　and install it by following the *User's Manual*.   
-
-##### RZ/V Software
-Download the *RZ/V2M Linux Package* and *DRP-AI Support Package* from the Software section in [RZ/V2M Software](https://www.renesas.com/rzv2m) and **build image/SDK** according to the *DRP-AI Support Package Release Note* *1.  
-
-*1 OpenCV library is required to run application example provided in this repository ([Application Example](./apps)).  
-To install OpenCV, please see [How to install OpenCV](./apps#how-to-install-opencv-to-linux-package) in Application Example page.  
-
-### Installing DRP-AI TVM[^1]  
-Before installing DRP-AI TVM[^1], please install the software listed in [Requirements](#requirements) and build image/SDK with RZ/V2M Linux Package and DRP-AI Support Package.  
-
-#### 1. Clone the respository.   
-```sh
-git clone --recursive -b v0.1 <git url.> drp-ai_tvm 
-```
-
-#### 2. Set environment variables.  
-Run the following commands to set environment variables.
-Note that environment variables must be set every time when opening the terminal.
-```sh
-export TVM_HOME=<.../drp-ai_tvm>/tvm                # Your own path to the cloned repository.
-export PYTHONPATH=$TVM_HOME/python:${PYTHONPATH}
-export SDK=</opt/poky/2.4.3>                        # Your own RZ/V2M Linux SDK path.
-export TRANSLATOR=<.../drp-ai_translator/>          # Your own DRP-AI Translator path.
-```
-#### 3. Install the minimal pre-requisites.   
-```sh
-# Install packagess
-apt update
-DEBIAN_FRONTEND=noninteractive apt install -y software-properties-common
-add-apt-repository ppa:ubuntu-toolchain-r/test
-apt update
-DEBIAN_FRONTEND=noninteractive apt install -y build-essential cmake \
-libomp-dev libgtest-dev libgoogle-glog-dev libtinfo-dev zlib1g-dev libedit-dev \
-libxml2-dev llvm-8-dev g++-9 gcc-9 wget
-
-apt-get install -y python3-pip
-pip3 install --upgrade pip
-apt-get -y install unzip vim
-pip3 install decorator attrs scipy numpy pytest onnx==1.9.0
-pip3 install torch==1.8.0 torchvision==0.9.0
-```
-If an already recent gcc/g++ compiler is already the default one in the system then the next step is not necessary. If the system default version is **9 or higher** then we are ready. If that is not the case then we can make sure that gcc/g++-9 will be the default compiler by running:
-```sh
-# Update gcc to 9.4
-update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-7 10
-update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-9 20
-update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-7 10
-update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-9 20
-update-alternatives --install /usr/bin/cc cc /usr/bin/gcc 30
-update-alternatives --set cc /usr/bin/gcc
-update-alternatives --install /usr/bin/c++ c++ /usr/bin/g++ 30
-update-alternatives --set c++ /usr/bin/g++
-update-alternatives --set gcc "/usr/bin/gcc-9"
-update-alternatives --set g++ "/usr/bin/g++-9"
-```
-Installing ONNX Runtime Library from precompiled release package.
-```sh
-# Install onnx runtime
-wget https://github.com/microsoft/onnxruntime/releases/download/v1.8.1/onnxruntime-linux-x64-1.8.1.tgz -O /tmp/onnxruntime.tar.gz
-tar -xvzf /tmp/onnxruntime.tar.gz -C /tmp/
-mv /tmp/onnxruntime-linux-x64-1.8.1/ /opt/
-```
-Setup DRP-AI TVM[^1] environment.
-```sh
-cd <.../drp-ai_tvm>
-bash setup/make_drp_env.sh
-```
-
-----
-
-## Deploy AI models on DRP-AI
-![drawing](./img/deploy_flow.png)  
-
-To deploy the AI model to DRP-AI on the target board, you need to compile the model with DRP-AI TVM[^1] to generate Runtime Model Data (Compile).  
-SDK generated from RZ/V Linux Package and DRP-AI Support Package is required to compile the model.  
-
-After compiled the model, you need to copy the file to the target board (Deploy).  
-You also need to copy the C++ inference application and DRP-AI TVM[^1] Runtime Library to run the AI model inference.  
-Moreover, since DRP-AI TVM[^1] does not support pre/post-processing of AI inference, OpenCV Library is essential.  
-
-Following pages show the example to compile the ResNet18 model and run it on the target board.  
-
-### Compile model with DRP-AI TVM[^1]
-Please see [Tutorial](./tutorials).  
-
-### Run inference on board
-Please see [Application Example](./apps) page.  
-
-### How-to
-Pages above only show the example for ResNet.  
-To find more examples, please see [How-to](./how-to) page.  
-It includes the sample to solve specific problems, i.e.;  
-- how to run application with camera;  
-- validation between x86 and DRP-AI, etc.  
-
-----
-For any enquiries, please contact Renesas.  
-
-[^1]: DRP-AI TVM is powered by EdgeCortix MERA™ Compiler Framework.
-
+# Extension package of TVM Deep Learning Complier for Renesas DRP-AI accelerators powered by EdgeCortix MERA&trade;
+
+[TVM Documentation](https://tvm.apache.org/docs) |
+[TVM Community](https://tvm.apache.org/community) |
+[TVM github](https://github.com/apache/tvm) |
+
+
+DRP-AI TVM[^1] is Machine Learning Compiler plugin for [Apache TVM](https://github.com/apache/tvm/) provided by Renesas Electronics Corporation.
+
+## License
+(C) Copyright EdgeCortix, Inc. 2022   
+(C) Copyright Renesas Electronics Corporation 2022   
+Contributors Licensed under an Apache-2.0 license.   
+
+## Supported Embedded Platforms
+- Renesas RZ/V2MA Evaluation Board Kit
+
+## Introduction
+### Overview   
+This compiler stack is an extension of the DRP-AI Translator to the TVM backend. CPU and DRP-AI can work together for the inference processing of the AI models.  
+
+<img src=./img/tool_stack.png width=350>   
+
+
+### File Configuration
+| Directory | Details |
+|:---|:---|
+|tutorials |Sample compile script|
+|apps |Sample inference application on the target board|
+|setup | Setup scripts for building a TVM environment |
+|obj |Pre-build runtime binaries|
+|tvm | TVM repository from github |
+|3rd party | 3rd party tools |   
+|how-to |Sample to solve specific problems, i.e., How to run validation between x86 and DRP-AI|
+
+
+## Installation
+### Requirements
+Requirements are listed below.  
+- OS : Ubuntu 20.04  
+- Python : 3.8  
+- Package : git
+- Evaluation Board: RZ/V2MA EVK
+- Related Software Version:
+    - [DRP-AI Translator v1.80](#drp-ai-translator)  
+    - [RZ/V2MA Linux Package v1.0.0](#rzv-software)  
+    - [RZ/V2MA DRP-AI Support Package v7.20](#rzv-software)  
+
+##### DRP-AI Translator
+Download the DRP-AI Translator v1.80 from the Software section in [DRP-AI](https://www.renesas.com/application/key-technology/artificial-intelligence/ai-accelerator-drp-ai#software) and install it by following the *User's Manual*.   
+
+##### RZ/V Software
+Download the *RZ/V2MA Linux Package* and *DRP-AI Support Package* from [Renesas Web Page](https://www.renesas.com/application/key-technology/artificial-intelligence/ai-accelerator-drp-ai) and **build image/SDK** according to the *DRP-AI Support Package Release Note* *1.  
+
+### Installing DRP-AI TVM[^1]  
+Before installing DRP-AI TVM[^1], please install the software listed in [Requirements](#requirements) and build image/SDK with RZ/V2MA Linux Package and DRP-AI Support Package.  
+
+#### 1. Clone the respository.   
+```sh
+git clone --recursive https://github.com/renesas-rz/rzv_drp-ai_tvm.git drp-ai_tvm
+```
+
+#### 2. Set environment variables.  
+Run the following commands to set environment variables.
+Note that environment variables must be set every time when opening the terminal.
+```sh
+export TVM_HOME=<.../drp-ai_tvm>/tvm                # Your own path to the cloned repository.
+export PYTHONPATH=$TVM_HOME/python:${PYTHONPATH}
+export SDK=</opt/poky/3.1.14>                       # Your own RZ/V2MA Linux SDK path.
+export TRANSLATOR=<.../drp-ai_translator/>          # Your own DRP-AI Translator path.
+```
+#### 3. Install the minimal pre-requisites.   
+```sh
+# Install packagess
+apt update
+DEBIAN_FRONTEND=noninteractive apt install -y software-properties-common
+add-apt-repository ppa:ubuntu-toolchain-r/test
+apt update
+DEBIAN_FRONTEND=noninteractive apt install -y build-essential cmake \
+libomp-dev libgtest-dev libgoogle-glog-dev libtinfo-dev zlib1g-dev libedit-dev \
+libxml2-dev llvm-8-dev g++-9 gcc-9 wget
+
+apt-get install -y python3-pip
+pip3 install --upgrade pip
+apt-get -y install unzip vim
+pip3 install decorator attrs scipy numpy pytest onnx==1.9.0
+pip3 install torch==1.8.0 torchvision==0.9.0
+```
+
+Installing ONNX Runtime Library from precompiled release package.
+```sh
+# Install onnx runtime
+wget https://github.com/microsoft/onnxruntime/releases/download/v1.8.1/onnxruntime-linux-x64-1.8.1.tgz -O /tmp/onnxruntime.tar.gz
+tar -xvzf /tmp/onnxruntime.tar.gz -C /tmp/
+mv /tmp/onnxruntime-linux-x64-1.8.1/ /opt/
+```
+Setup DRP-AI TVM[^1] environment.
+```sh
+cd <.../drp-ai_tvm>
+bash setup/make_drp_env.sh
+```
+
+----
+
+## Deploy AI models on DRP-AI
+![drawing](./img/deploy_flow.png)  
+
+To deploy the AI model to DRP-AI on the target board, you need to compile the model with DRP-AI TVM[^1] to generate Runtime Model Data (Compile).  
+SDK generated from RZ/V Linux Package and DRP-AI Support Package is required to compile the model.  
+
+After compiled the model, you need to copy the file to the target board (Deploy).  
+You also need to copy the C++ inference application and DRP-AI TVM[^1] Runtime Library to run the AI model inference.  
+
+Following pages show the example to compile the ResNet18 model and run it on the target board.  
+
+### Compile model with DRP-AI TVM[^1]
+Please see [Tutorial](./tutorials).  
+
+### Run inference on board
+Please see [Application Example](./apps) page.  
+
+### How-to
+Pages above only show the example for ResNet.  
+To find more examples, please see [How-to](./how-to) page.  
+It includes the sample to solve specific problems, i.e.;  
+- how to run application with camera;  
+- validation between x86 and DRP-AI, etc.  
+
+### Error List
+If error occurred at compile/runtime operation, please refer [error list](./docs/Error_List.md).
+
+## Support
+If you have any questions, please contact [Renesas Technical Support](https://www.renesas.com/support).
+
+----
+For any enquiries, please contact Renesas.  
+
+[^1]: DRP-AI TVM is powered by EdgeCortix MERA™ Compiler Framework.
diff --git a/apps/CMakeLists.txt b/apps/CMakeLists.txt
@@ -8,14 +8,8 @@ include_directories(${TVM_ROOT}/3rdparty/dmlc-core/include)
 include_directories(${TVM_ROOT}/3rdparty/compiler-rt)
 
 set(TVM_RUNTIME_LIB ${TVM_ROOT}/build_runtime/libtvm_runtime.so)
-set(SRC tutorial_app.cpp MeraDrpRuntimeWrapper.cpp)
+set(SRC tutorial_app.cpp MeraDrpRuntimeWrapper.cpp PreRuntime.cpp)
 set(EXE_NAME tutorial_app)
 
 add_executable(${EXE_NAME} ${SRC})
 target_link_libraries(${EXE_NAME} ${TVM_RUNTIME_LIB})
-
-find_package(OpenCV REQUIRED)
-if(OpenCV_FOUND)
-    target_include_directories(${EXE_NAME} PUBLIC ${OpenCV_INCLUDE_DIRS})
-    target_link_libraries(${EXE_NAME} ${OpenCV_LIBS})
-endif()