General Information

Official RZ/V2H Device Website *
- Product Page
- Please review the **Documentation & Downloads** section

Evaluation Board Documentation
- RZ/V2H Quad-core Vision AI MPU Evaluation Kit

Software Downloads
- Check "Software & Tool Pages" section on on this page

- RZ/V2H Linux Package
- Mali Graphic Library
- Video Codec Library
- ARM Mali Full ISP
- RZ/V2L DRP-AI Support Package
- RZ/V2H Cortex-M33 and Cortex-CR8 Multi-OS Package (optional)
- Robotic Operating System (ROS)
- RZ/V2H Cortex CM33 FSP

Prebuilt Images

To help expedite evaluation of the RZ/V2H we provide Pre-build Images of the RZ/V2H VLP.

RZ/V2H AI SDK

AI SDK Contents

eSD Card Image

The eSD Card Image is a prebuilt yocto image that contains the following yocto packages

VLP Linux
ARM Mali Graphics ( limited license )
Codec ( limited license )
DRP-AI Driver
OpenCVA ( DRP Accelerated )

The eSD Image DOES NOT include the following

e-CON MIPI Camera ( Contact e-CON Systems )
AI SDK Demos ( Coming Soon )

TVM Translator Environment

The TVM Translator is the recommended translator to translate AI models for the DRP-AI Driver. All the AI SDK demos use the TVM Translator. Included in the AI SDK is the DRP-AI Translator. This translator is needed for TVM installation and does not need to be installed. Installation using Docker is recommended because the TVM Environment requires a PC running Ubuntu 20.04. For information about Docker setup see here.

For information about using the TVM Translator see the Official Site here.

NOTE: Once the AI SDK Environment is setup the TVM will be installed in the the root directory /drp_tvm_sdk

Available Demos

DRP-AI

vSLAM (Contact Sales Representative for demo)

DRP-AI Demos
- Included with the DRP-AI Package. AI models include
  - Classification (Resnet50)
  - Object Detection ( YoloX and Yolov2 )
  - Segmentation ( DeepLabv2 )
Renesas RZ/V2H AI SDK

DRP-AI Tools

AI Model to DRP-AI Hardware Translation Flow. For Sparse Model Creation the recommended path after ML Framework is S1, S2, S3. For Dense Model Creation after ML Framework is D1, D2.

⚠️ The DRP-AI Tools will only work in Ubuntu 20.04

⚠️ the flow S1 is using the pruning CLI tool. The Pruning tool also includes python expansion pack, so S1 can be integrated into the ML Framework training step.

(S1) RZ/V2H Expansion Pack (Pruning Tool )

The DRP-AI MAC supports sparse arrays (unstructured pruning). The expansion pack includes CLI and python libraries (expansion pack). The expansion pack allows for integration of the Pruning process into the ML Frameworks for training. The Expansion Pack supports Pytorch, Tensorflow, and Tensorflow-lite.

Official Site

(S2,3 or D1,2 ) RZ/V2H DRP-AI TVM

TVM Repository

DRP-AI TVM wiki

The Renesas TVM is the DRP-AI translator tool that converts standard ML Frameworks ( Pytorch, Tensorflow, ONNX, .. ) to DRP-AI Hardware object files. The TVM for RZ/V2H includes the following tools. This is the recommended tool for AI Model Translation to the DRP-AI.

Inference Pre-processing Configuration
Quantization Tool
Translation Compiler

RZ/V2H DRP-AI Translator and Quantization Tool

Official Site

This tool is used by the TVM for translation of AI Models. It is needed to to install the TVM. ( AI SDK includes this )

For Advance Translation this tool can also be used for AI Model translation instead of the Renesas TVM. Only AI Model operations listed in the RZ\V2H DRP-AI Translator Manual ( section 4.1) can be used with this translator. For AI models that have un-supported operations please use the Renesas DRP-AI TVM Tool. The tool supports quantization and translation of AI Models exported in the ONNX format to the RZ/V2H DRP-AI (INT8) hardware. This tool includes the following

RZ/V2H DRP-AI Translator
DRP-AI Quantization Tool

The RZV2H DRP-AI MAC only performs INT8 operations, so AI Models must be quantized before translation.

Retraining Quantized Models

It is recommended to retrains a model after quantization to reduce changes to the inference accuracy . For that purpose the Quantization tool includes optional parameters that point to the data set and dataset parsing function.

Beside the AI Model below are additional items needed to properly quantize a model.

Customer Dataset ( does not need to be the full dataset )
Function to parse the mode

Getting Started

The RZV2H AI SDK page is the recommended place to start evaluating the RZ/V2H EVK. The page includes detailed information about setup of the EVK, and of the AI SDK Development Environment. Building a Yocto BSP,. links to Yocto Packages and information is here..

RZ/V2H AI Applications Tutorial Getting Started V3.00 | Renesas

Setup AI SDK Environment and Evaluate Prebuilt Image

Step 1 Obtain EVK Board
- Links to the AI SDK and EVK
Step 2 Obtain Necessary Environment
- List Software and hardware requirments
Step 3 Obtain the AI SDK
- Download the RZ/V2H AI SDK
- AI SDK Source not needed for initial evaluation
- See Prebuilt Images for more information
Step 4 Setup SDK
Step 5 Build AI Application
- Requires TVM Runtime Library to run applications
  - Instructions steps 5 - 7 show how to get the library from AI SDK
  - TVM Runtime for RZV2H can be found here too.
  - Deploy TVM Runtime to Board
    - Section 2 "Deploy Application to the Board"
- Link to Demo Applications mentioned in this step
  - The link included information on how to build, deploy and run the application
  - More Demos see here.
Step 6 Deploy and Run Applications
- Follow the RZ/V2H EVK Getting Started page

Yocto Build

The Renesas AI SDK includes information about building the AI SDK using the Yocto Build Framework. This is recommended to customize the Linux BSP.

Download the AI SDK Source
Follow the direction "How to build RZ/V2H AI SDK Source Code"

The BSP includes the following packages

RZ/V2H Linux VLP
DRP-AI Drivers
DRP OpenCV Accelerator
Mali Graphics ( Restricted License )
Renesas Codec ( H.265/H.264 ) ( Restricted License )

Not included

Demos ( see here for information )
e-Con MIPI Driver ( see link )
- follow step 5 to install e-CON Camera
Unrestricted Mali Graphics and Codec License
- Request for Unrestricted Graphic Mali License
CM33 and CR7 Multi-OS Support and FSP ( Coming Soon )

CM33 and CR7 Multi-OS and FSP

This has not been released for RZ/V2H yet.

Hardware

Power Supply

Recommended Power Supplies ( Recommend 100Ws )


	Description	Link	Comments
🙂	Amazon Basics	Link	2xUSB-C, 2xUSB, 1x65-100W, 1x18W, 2x12W
🙂	Nekteck	Link	1x USBC 100W

+LCD Monitors

LCD Monitors
	Resolution	Touchscreen	Description	Link	Comments
🙂	1920x1080	Yes	Wimaxit 14 Inch	🛒 buy	This is a very nice screen. Good color and easy to press the icons. The LCD input is mini-HDMI and comes with a mini-HDMI to HDMI cable. The RZ board is micro-HDMI. You will also need to buy a HDMI to micro-HDMI adapter. Or, buy a mini-HDMI to HDMI-micro cable. Note the the micro-USB connection on the LCD for touchscreen does not work, but the USB-C connector does work for touchscreen. You will simply need a USB-C to USB-A cable or adapter.
🙂	1920x1080	Yes	ELECROW 10.1 Inch	🛒 buy	This is a very nice screen. You will also need to buy a HDMI to micro-HDMI cable.
🙂	1280x800	Yes	SunFounder 10.1 inch	🛒 buy	All the cables that you will need are included.
🙂	1024x600	Yes	Ingcool 7 inch 7IP-CAPLCD	🛒 buy	If you attach the 'Touch' USB connection on the LCD to the RZ/G2L board, it will both power the LCD board as well as enable touch support. This kit comes with HDMI cables.
🙂	800x480	Yes	GeeekPi 5 Inch EP-0081	🛒 buy	HDMI cable not included.

MIPI Camera

Supported MIPI Cameras


	Resolutions	Description	Link	Comments
🙂	VGA ( 640x480) , HD ( 1270x720), FHD(1920,1080) Format YUYV FPS 30 and 60	e-CAM22_CURZH	Link	This Camera was designed for the EVK MIPI Connector Interface. Uses the Sony Starvis I IMX462.