The RZ/V2H high-end AI MPU boasts Renesas' proprietary dynamically reconfigurable processor AI accelerator (DRP-AI3), quad Arm® Cortex®-A55 (1.8GHz) Linux processors, and dual Cortex®-R8 (800MHz) real-time processors. Furthermore, the RZ/V2H also includes another dynamically reconfigurable processor (DRP). This processor can accelerate image processing, such as OpenCV, and dynamics calculations required for robotics applications. It also features high-speed interfaces like PCIe®, USB 3.2, and Gigabit Ethernet, making it an ideal microprocessor for applications such as autonomous robots and machine vision in factory automation, where advanced AI processing must be implemented with low power consumption.
The RZ/V2H EVK provides a USB serial interface, 2 channel Ethernet interfaces, four camera interfaces and an HDMI display interface, in addition to many other interfaces (PMOD, microphone, audio output, etc.). The RZ/V2H EVK can be acquired directly through the Renesas website.
The Renesas RZ/V2H board realizes hardware acceleration through the DRP-AI IP that consists of a Dynamically Configurable Processor (DRP), and Multiply and Accumulate unit (AI-MAC). The DRP-AI IP is designed to process the entire neural network plus the required pre- and post-processing steps. Additional optimization techniques reduce power consumption and increase processing performance. This leads to high power efficiency and allows using the MPU without a heat sink.
Note that, the DRP-AI is designed for feed-forward neural networks that are usually in vision-based architectures. For more information about the DRP-AI, please refer to the white paper published by the Renesas team.
The Renesas tool “DRP-AI TVM” is used to translate machine learning models and optimize the processing for DRP-AI. The tool is fully supported by Edge Impulse. This means that machine learning models downloaded from the studio can be directly deployed to the RZ/V2H board.
For more technical information about RZ/V2H, please refer to the Renesas RZ/V2H documentation and for the RZ/V2H-EVK.
Renesas provides Yocto build system to build all the necessary packages and create the Linux image. The Renesas documentation calls out that the build system must be based off of Ubuntu 20.04. The following instructions here outline the necessary steps to setup your build environment.
In order to use the Edge Impulse CLI tools, NodeJS v18 needs to be installed into the yocto image that you build. You will need to download the required NodeJS v18 patch here. Given the instructions called out here, once the following file must be downloaded from Renesas (specific versions specified are required):
After downloaded, you should have these two files in your directory:
Next, you will need to create and patch your V2H yocto build environment as follows (this can be exported into a script that can be run):
You can then invoke your V2H yocto build process via:
Renesas documentation here then shows you different build options + how to flash your compiled images onto your V2H board. Once your build completes, your files that will be used in those subsequent instructions called out here to flash your V2H board can be found here:
screenThe easiest way is to connect through serial to the RZ/V2H board using the USB mini b port.
After connecting the board with a USB-C cable, please power the board.
Power on the board: Connect the power cable to the board, switch SW3 ON then SW2 ON.
Please install screen to the host machine and then execute the following command from Linux to access the board:
You will see the boot process, then you will be asked to log in:
Log in with username root
There is no password
Note that, it should be possible to use an Ethernet cable and log in via SSH if the daemon is installed on the image. However, for simplicity purposes, we do not refer to this one here.
Once you have logged in to the board, please run the following command to install Edge Impulse Linux CLI
With all software set up, connect your USB camera (or a supported MIPI CSI camera) to your Renesas board (see 'Next steps' further on this page if you want to connect a different sensor), and run:
This will start a wizard which will ask you to log in and choose an Edge Impulse project. If you want to switch projects run the command with --clean.
That's all! Your device is now connected to Edge Impulse. To verify this, go to your Edge Impulse project, and click Devices. The device will be listed here.
Currently, all Edge Impulse models can run on the RZ/V2H CPU which is a dedicated Cortex A55. In addition, you can bring your own model to Edge Impulse and use it on the device. However, if you would like to benefit from the DRP-AI3 hardware acceleration support including higher performance and power efficiency, please use one of the following models:
For object detection:
Yolov5 (v5)
FOMO (Faster objects More Objects)
For Image classification:
MobileNet v1, v2
It supports as well models built within the studio using the available layers on the training page.
Note that, on the training page you have to select the target before starting the training in order to tell the studio that you are training the model for the RZ/V2H. This can be done on the top right in the training page.
If you would like to do object detection with Yolov5 (v5) you need to fix the image resolution in the impulse design to 320x320, otherwise, you might risk that the training fails.
With everything set up you can now build your first machine learning model with these tutorials:
If you are interested in using the EON tuner in order to improve the accuracy of the model this is possible only for image classification for now. EON tuner supports for object detection is arriving soon. For more information see EON Tuner.
To run your impulse locally, just connect to your Renesas RZ/V2H and run:
This will automatically compile your model with full hardware acceleration and download the model to your Renesas board, and then start classifying.
Or you can select the RZ/V2H board from the deployment page, this will download an eim model that you can use with the above runner as follows:
Go to the deployment page and select:
Then run the following on the RZ/V2H:
You will see the model inferencing results in the terminal also we stream the results to the local network. This allows you to see the output of the model in real-time in your web browser. Open the URL shown when you start the runner and you will see both the camera feed and the classification results.
Since the RZ/V2H benefits from hardware acceleration using the DRP-AI, we provide you with the drp-ai-tvm-i8 library that uses our C++ Edge Impulse SDK, DRP-AI TVM and models headers that run on the hardware accelerator. If you would like to integrate the model source code into your applications and benefit from the DRP-AI then you need to select the drp-ai-tvm-i8 library.
We have an example showing how to use the drp-ai-tvm-i8 library that can be found in Deploy your model as a DRP-AI TVM i8 library.
