Hardware Components

We will explore two distinct hardware platforms: the OpenMV Cam H7 and the TinyML Kit.

While we focus on these specific devices, the concepts covered here are broadly applicable to other Arm Cortex-based microcontrollers.

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1. OpenMV Cam H7

The OpenMV Cam H7 is a powerful, small form factor embedded vision camera module.

• Processor: Features an ARM Cortex-M7 core running at 480 MHz, enabling efficient high-speed inferencing.
• Vision: Ideal for real-time computer vision applications due to its high clock speed and optimized architecture.
• Integration: Boasts a high-quality image sensor and versatile interfaces for seamless device integration.

2. TinyML Kit

Developed collaboratively by Arduino and Edge Impulse, the TinyML kit empowers developers to create and deploy machine learning models on low-power microcontrollers.

The kit consists of three main components:

a) Arduino Nano 33 BLE Sense

• Core: A compact microcontroller board featuring the Nordic nRF52840 (32-bit ARM® Cortex®-M4 CPU @ 64 MHz).
• Connectivity: Built-in Bluetooth Low-Energy (BLE) module.
• Sensors: Equipped with a rich array of sensors including accelerometer, gyroscope, magnetometer, temperature, humidity, pressure, and a microphone. This variety allows for diverse data collection and ML applications.

b) OV7675 Camera Module

• A compact, low-cost camera sensor capable of capturing images and video.
• Resolution: 640x480 pixels.
• Interface: Communicates with the Arduino Nano via the I2C interface.

c) Tiny Machine Learning (TinyML) Shield

• A specially designed PCB that simplifies hardware connections.
• Eliminates manual wiring by providing a dedicated header for the OV7675 camera module and correct pinouts for the Arduino Nano.

TinyML Kit Assembly Guide