The RZ/V2MA evaluation board may not be delivered with the latest IPL and uboot loaders. Please updated the board with the generated Yocto files. After following the Getting Started instructions the following files will be generated,
Instructions to load these IPL files is located in the RZV2MA Linux Getting Started Guide.
We have a linux bash script to automate this.
Image RemovedImage AddedSet board to Forced Write Mode
Step 1) This script requires that the flashwriter tool is loaded on the board. Set the board mode switch to "Forced Write Mode". Then follow section 4.2.3 to load the flash writer before running the script. Step 2) Navigate to the directory where the RZ/V2MA kernal, filesystem, and ipl files are deployed. Below is example of where this is.
/rzv2ma/build/tmp/deploy/images/rzv2ma
Step 3) Copy this script to that directory
Step 4) Set the script as executable
chmod +x Flash_loader.sh
Step 5) Execute the following command.
./Flash_loader.sh .
1. Software BSP Components
There is total of 4 packages to build the RZ/V2MA BSP. Up to 5 packages are used to build the BSP. Please download from the Official RZ/V2MA Website. Please look in the "Software Downloads" section.
Copy all ZIP file to a Linux PC.
The evaluation versions contain a time limitation that stops the software after a few hours.
2. Build Environment
3. Build the BSP
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These instructions are a summary of what is explained in the package Release Notes. export WORK_DIR=$PWD/rzv2ma export SRC_DIR=../ mkdir $WORK_DIR mkdir $WORK_DIR cd $WORK_DIR ### Extract the BSP Linux package (r01an6514ej<version>-rzv2ma-linux.zip) unzip $SRC_DIR/r01an6514ej0111-rzv2ma-linux.zip tar -xf ./r01an6514ej0111-rzv2ma-linux/bsp/rzv2ma_bsp_eva_v100.tar.gz rm -rfd r01an6514ej0111-rzv2ma-linux ### Copy/Move the 'RZV2MA Codec Library v0.96' Zip file (RTK0EF0131F02000SJ-<version>.zip) under the BSP directory. unzip $SRC_DIR/RTK0EF0131F02000SJ-v0.96.zip tar zxvf meta-rz-features.tar.gz ### Copy/Move the DRP Support archive file ( r11an0592ej0731-rzv2ma-drpai-sp.zip ) ### Extract the 'DRP-AI Driver Support' package file (meta-rz-features.tar.gz) under the rzv2ma_drpai-driver directory. unzip $SRC_DIR/r11an0592ej0731-rzv2ma-drpai-sp -d drp tar -xvf drp/rzv2ma_drpai-driver/meta-rz-features.tar.gz rm -rfd drp ### Copy/Move the OpenCV Accelerator archive file ### Extract the 'OpenCV Accelerator' pacage file (meta-rz-features.tar.gz) unzip $SRC_DIR/r11an0650ej0120-rzv2ma-opencv-accelerator-sp.zip tar -xvf .r11an0650ej0120-rzv2ma-opencv-accelerator-sp/meta-rz-features.tar.gz rm -rfd r11an0650ej0120-rzv2ma-opencv-accelerator-sp ### Setup the Yocto Enviroment variables cd $WORK_DIR source ./poky/oe-init-build-env ### Copy the Renesas Yocto Layer files to build directory cd $WORK_DIR/build cp ../meta-renesas/docs/template/conf/rzv2ma/*.conf ./conf/ cd $WORK bitbake core-image-bsp bitbake core-image-bsp -c populate_sdk |
4. Prepare an SD Card
$ wget https://raw.githubusercontent.com/renesas-rz/rzg2_bsp_scripts/master/usb_sd_partition/usb_sd_partition.sh $ chmod +x usb_sd_partition.sh $ ./usb_sd_partition.sh
# Change to the Yocto output directory that contains the files $ cd build/tmp/deploy/images/smarc-rzv2l # Copy the Linux kernel and Device Tree to partition 1 $ sudo cp -v Image /media/$USER/RZ_FAT $ sudo cp -v r9a07g054l2-smarc.dtb /media/$USER/RZ_FAT # Copy and expand the Root File System to partition 2 $ sudo tar -xvf core-image-weston-smarc-rzv2l.tar.gz -C /media/$USER/RZ_ext (or if NOT using graphics) $ sudo tar -xvf core-image-minimal-smarc-rzv2l.tar.gz -C /media/$USER/RZ_ext # Make sure all files are finished writing before removing the USB card reader from the PC $ sync
5. Board Switch, LED and Serial Port information
Image Added | Led Placement | Image RemovedImage Added Switch Information | Image RemovedImage Added USB Serial Communication Port |
6. Power the Board and Connect to the Serial Port
7. Boot the Board
The AI Translator program provides a easy method to translate high end AI programming languages ( PyTorch, Tensorflow, Caffee, etc. ) to run on the Renesas DRP-AI hardware. The translator does this be translating the global standard ONNX format. High level AI languages support export of the AI to ONNX format.
The AI Translator and DRP-AI is designed for Vision based AI.
AI Translator Release Note Document shows the supported AI operators.
Official RZ/V2MA AI Translator Website here
For additional technical support for the AI Translator refer to this DRP-AI Translator wiki page here.
Note: This Translator is for the RZ/V2MA only refer to AI Translator page.
While the AI Translator (see section above) can translate ONNX models to the DRP-AI hardware, it is restricted by the supported AI operations. This can restrict the number of supported AI Models. The TVM Translator expands the number of supported AI models for the RZ/V processors (currently RZ/V2MA). The TVM translates ONNX models by delegating the generated output between the DRP-AI and CPU.
Official RZ/V2MA TVM Translator Github repo..
This is the TVM Software framework based on the Apache TVM. The TVM includes python support libraries, and sample scripts. The python scripts follow the Apache TVM framework API found here.
For more information try this wiki page
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