A model for implementing artificial intelligence into embedded systems

This paper examines the problem of implementing artificial intelligence in resource-constrained embedded systems. A comprehensive analysis of existing hardware platform limitations and the challenges of integrating AI into devices with limited computing power, RAM, and energy efficiency is provided. This analysis represents a significant practical challenge in modern IoT devices, robotics, and industrial automation. The process of implementing an image classifier model trained on MNIST on a Raspberry Pi Pico microcontroller platform is described in detail. A unified methodology for implementing AI in embedded systems is proposed, covering problem definition, hardware platform selection, model selection and optimization, offline development environment setup, downloading, and testing. This methodology has practical value and can be adapted to a wide range of IoT projects.