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Welcome to EAI Project documentation website.

Here at our documentation website, we aim to provide you with comprehensive information and resources related to embedded AI, frameworks, optimizations, and applications. Whether you are a developer, researcher, or enthusiast, you will find something useful here to enhance your understanding and skills.

About Embedded AI

Embedded AI refers to the integration of artificial intelligence algorithms and models into embedded systems such as microcontrollers, FPGAs, and SoCs. It enables intelligent decision-making and autonomous operation of the embedded devices without relying on cloud or network connectivity.

Frameworks and Libraries

We cover a wide range of frameworks and libraries for embedded AI development, including TensorFlow Lite, Keras, PyTorch, and more. These tools help simplify and accelerate the process of building and deploying AI models on embedded devices.

Optimizations

Optimizations are crucial for embedded AI applications, as they help improve the efficiency, speed, and accuracy of the models. We provide guidance on various optimization techniques such as quantization, pruning, compression, and hardware acceleration.

Applications

Embedded AI has a wide range of applications, from smart homes and IoT devices to healthcare and automotive. We showcase real-world examples of embedded AI applications and provide insights into their design, development, and deployment. (in progress)

Hardware study

(in progress)