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Product description: EdgeBadge - TensorFlow Lite - mini console for microcontrollers - Adafruit 4400
EdgeBadgemini console manufactured byAdafruit. It has aTFTdisplaywith1.8''diagonal and160 x 128 pxresolution. The console contains8 buttonsfor control, which are finished with silicone caps. The module is equipped with 5 NeoPixelLEDs, signaling activity during the game, and with a number ofsensors: 3-axis accelerometer, light sensor, as well as a mini speaker with buzzer and Mono Class-D speaker driver.
EdgeBadge mini console with 1.8'' TFT display.
EdgeBadge technical specifications
- Chip: ATSAMD51J19
- power supply: 3.3 V
- clocking: 120 MHz
- Flash memory: 512 kB
- SPI Flash memory: 2 MB
- RAM: 193 kB
- Display:
- technology: TFT
- diagonal: 1,8''
- resolution: 160 x 128 px
- communication interface: SPI
- Control: 8 buttons for control with silicone button caps
- Status indication: 5 NeoPixels LEDs
- Built-in sensors:
- Motion sensor: 3-axis accelerometer
- light sensor
- mini speaker with buzzer
- Mono Class-D speaker driver
- Ports:
- Li-Pol battery port with built-in charging function
- USB port for battery charging, programming and debugging
- two FeatherWings compatible female strips
- JST ports
- RESET utility button
- ON / OFF switch
- Mini console dimensions: 86.3 x 54.3 x 13.6 mm
- Weight: 29.6 g
EdgeBadge programming
The EdgeBadge console from Adafruit can be programmed in three ways:
- MakeCode Arcade- the easiest runtime program that allows the user to create games using block programming and load them through the bootloader.
- CircuitPython- is a support for a wide range of sensors that allows you to create custom, proprietary graphics, play wave files, and also allows you to create text in any font.
- TensorFlow Lite for Microcontrollers / Arduino- also designed to run machine learning models on microcontrollers and other modules. TensorFlow Lite for Microcontrollers is written in C++ and requires a 32-bit platform. The framework is available as an Arduino library.
Extensive programming capabilities due to support by the most popular platforms: MakeCode, CircuitPython and Arduino.
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