Description
This Arduino GIGA R1 WiFi Development Board measures only 101 x 53mm and has the same form factor as the popular Mega and Due. Despite its small size, it offers 76 GPIO pins, which include 12 analog pins, 12 PWM pins, 4 UARTs, 3 I2C ports, 2 SPI ports, 1 FDCAN, and 1 SAI. Additionally, Wi-Fi and Bluetooth LE wireless communications are supported by an external antenna Murata 1DX, so users can quickly connect the board to the Arduino IoT Cloud and remotely monitor their project without concern about communication security.
The GIGA R1 WiFi employs dual-core 32-bit Arm MCU STM32H747XI, bringing together a Cortex-M7 at 480 MHz and a Cortex-M4 at 240 MHz. Besides, it comes with 2MB Flash and 1MB RAM, which allow users to run two programs simultaneously, for example, run micro-python in one and Arduino in the other.
Figure: Board Overview of Arduino GIGA R1 WiFi
The board features a USB-C connector used for power, programming, and an HID function that allows it to simulate mouse or keyboard when connected to a PC. And it also has a USB-A connector suitable for hosting USB sticks, other mass storage devices, and HID devices such as a mouse or keyboard. Additionally, it includes a 3.5mm input/output audio jack, JTAG connector(2x5 1.27mm), and 20Pin Arducam camera connector. The GIGA R1 WiFi can handle up to 24 volts.
Figure: Arduino GIGA R1 WiFi Pinout
Figure: Arduino GIGA R1 WiFi Block Diagram
Features
Applications
Specifications
- Audio: 3.5mm audio jack
- I/O: I/O pins (includes camera/display pins) × 76
- UART × 4, I2C × 3, SPI × 2
- PWM × 12
- Analog inputs × 12
- DAC × 2
- CAN bus (requires an external transceiver)
- Operating Voltage: 3.3V
- Current per I/O Pin: 8mA
- Debugging: JTAG connector
- Input Voltage: 6-24V
- Dimensions: about 101×53 mm/3.98×2.09"
Projects
GIGA R1 WiFi-powered wearable detects falls using a Transformer model
Naveen Kumar set out to produce a wearable fall-detecting device that aims to increase the speed at which this occurs by utilizing a Transformer-based model rather than a more traditional recurrent neural network (RNN) model.