The Pico4ML TinyML Dev Kit from Arducam is a single board system powered by Raspberry Pi's RP2040 microcontroller. It has the power to run all Tensorflow Lite Micro tiny machine learning examples. The board features a QVGA camera module with ultra-low power consumption, configurable 1-bit video data serial interface with video frame and line sync, and the monochrome sensor makes image processing an easy part for most machine vision applications.
The audio chip on the Pico4ML is capable of directly outputting PDM (Pulse-density modulation) signals, this integration allows the RP2040 to receive audio input. Motion tracking is also a built-in feature with a 2.5 mW low-power 9-axis IMU.
The small TFT display at the back of Pico4ML is a 160×80 LCD, it’s connected to the board through the SPI interface, you can do a live preview of the camera, or display the results of any of the your ML models in real-time.
The Arducam Pico4ML is completely open-source, all its codes, design files, and schematics will be made available for anyone to use, rebuild or modify.
If a board needs code or communicates somehow, you're going to need to know how to program or interface with it. The programming skill is all about communication and code.
Skill Level: Competent - The toolchain for programming is a bit more complex and will examples may not be explicitly provided for you. You will be required to have a fundamental knowledge of programming and be required to provide your own code. You may need to modify existing libraries or code to work with your specific hardware. Sensor and hardware interfaces will be SPI or I2C.
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If it requires power, you need to know how much, what all the pins do, and how to hook it up. You may need to reference datasheets, schematics, and know the ins and outs of electronics.
Skill Level: Rookie - You may be required to know a bit more about the component, such as orientation, or how to hook it up, in addition to power requirements. You will need to understand polarized components.
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