Avnet ZUBoard 1CG Development Board Hands-On - A New Choice for Deep Learning
summaries
This article introduces the features, architecture, hardware units and other concepts of the Avnet ZUBoard 1CG development board, and provides a basic introduction on how to connect the board using the Ethernet and serial interfaces, supplemented by two application examples to demonstrate its functionality.
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The Best Object-Oriented Programming Tutorials on the Web for Getting Started: 00 Introduction to Object-Oriented Design Methods
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The Best Object-Oriented Programming Tutorials for Getting Started on the Web: 02 Python Implementations of Classes and Objects - Creating Classes with Python
The Best Object-Oriented Programming Tutorials for Getting Started on the Web: 03 Python Implementations of Classes and Objects - Adding Attributes to Custom Classes
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main body (of a book)
The Avnet ZUBoard 1CG is an FPGA development board based on the RISC-V architecture:
Its key features include:
Overall, ZUBoard is a development board based on the open RISC-V architecture with flexibility, scalability and rich peripheral support for a wide range of embedded and IoT applications. It provides developers with a powerful hardware platform and rich development support.
1. Areas of application
In the market, the ZUBoard 1CG is positioned in the field of high-performance embedded applications and is particularly suited to application scenarios such as machine learning, image processing, edge computing and IoT, where powerful processing power and flexibility are required.
2. Development board hardware unit
2.1 processing unit
Zynq UltraScale+ MPSoC with integrated dual-core A53 APU and dual-core R5 RPU It is responsible for handling most of the computing tasks.
2.2 Memory and Storage
The chips next to it include DDR4 memory and QSPI flash memory for storing data and the operating system.
2.3 peripheral interface
- JTAG/UART (J11):: For debugging and serial communications
- USB 2.0 Type A Port (J13): Can be connected to peripherals such as a mouse, keyboard or storage device
- 15V USB-C power input (J15): Powering the board
- Ethernet Interface (J11): Gigabit Ethernet support
2.4 User buttons and switches
- User keys (SW1, SW3): User-defined keys that can be used to control the behavior of the program
- User Slide Switch (SW4): 4-position MIO user switch, user can set the switch state as required
- Reset button (SW6): For resetting the system
- Power Switch (SW7): Control board on/off
Demonstration section
Next, I'll teach you how to get started with ZUBoard in 2 minutes. I've prepared 2 demos for you, the first one is about digital recognition of gestures with image processing, and the second one is about gesture-controlled software robots.(Both examples from hackster)Before running the demo, let's finish deploying the environment and connecting to the development board via Ethernet.
3.1 Environmental deployment
First, connect the SD card to the front of the computer with a card reader:
Format the SD card:
Write the image file (the image file can be downloaded from the Baidu.com disk link in the reference section):
3.2 Connecting to the Development Board via Ethernet
Wired and connected to a computer with a primary power input of 15V:
After power-up, the on-board LEDs will light up:
Press the SW7 power on button, the white light will be on to start the power on:
The blue light is on, indicating successful power-up:
Download the MobaXterm software (the download URL is available at the end of the text):
Select the serial port for the USB connection:
Set the baud rate to 115200:
Turn off flow control:
Use ifconfig to view the network IP address:
Configure the SSH service to automatically access the internal system, after logging in, we will be inside the system and can start working on programming and configuration. Copy the network IP address into the box for the remote host:
Check Sprcify username and enter root:
The following page will pop up automatically, and we are now successfully connected to the board via Ethernet:
3.3 ASL Gesture Digit Recognition Demo
-
Switch to the project directory ****:pass (a bill or inspection etc)
cd asl_classification_vitis_ai
command to switch to the project directory -
Run the real-time gesture digit recognition script ****:fulfillment
python3 asl_classify_live.py --model=./model_mobilenetv2/B{#}/asl_classifier.xmodel
Command to run real-time gesture digit recognition scripts
Next, we can realize the recognition of gesture numbers!
3.4 Gesture Control Software Robot Demo
Perform the following steps:
4. User experience
The design of the ZUBoard 1CG makes it an ideal platform for exploring bare-metal programs, Linux or Vitis AI gas pedals. Its powerful processing capabilities and rich interface resources enable developers to easily implement complex embedded applications. The integrated temperature and pressure sensors on the development board add environmental data acquisition capabilities to the system, thus extending its application range. In addition, with its rich expansion interfaces, the ZUBoard 1CG can be easily connected to a variety of peripherals, further enhancing its flexibility and scalability.
bibliography
- American Sign Language Recognition (ASL)./AlbertaBeef/asl-classification-with-vitis-ai-025765#toc-executing-the-demo-with-remote-display--mobaxterm-5
- Gesture-controlled software robots:/AlbertaBeef/controlling-robots-with-zuboard-b6d13b
- MobaXterm software download site:/
- Need to download the software and the link to the img file netbook:/s/1fMm9uYWYPWTFoMahdneFlg?pwd=1234