Real-Time Systems for Embedded AI

Real-Time Systems for Embedded AI provides an in-depth exploration of the integration of artificial intelligence within real-time operating systems. Participants will engage in hands-on projects that emphasize the design, implementation, and optimization of embedded AI applications. The course is structured to facilitate practical learning through interactive sessions, enabling participants to apply theoretical concepts in real-world scenarios. By the end of the program, learners will have developed a comprehensive understanding of how to create responsive and efficient systems that utilize AI technologies.
The curriculum is designed to cover essential topics that are critical for professionals in the field. Participants will delve into the architecture of real-time systems, explore various RTOS options, and learn to implement AI algorithms tailored for embedded environments. The course culminates in a final project where learners will create a fully functional embedded AI application, which they are encouraged to publish in Cademix Magazine, showcasing their innovative solutions and technical skills.
Real-time system fundamentals
Overview of Real-Time Operating Systems (RTOS)
AI algorithms suitable for embedded systems
System architecture design for real-time applications
Resource management in real-time systems
Scheduling algorithms for RTOS
Sensor integration and data acquisition
Communication protocols in embedded AI
Performance evaluation techniques
Final project: Development of an embedded AI application

Equip participants with the skills to design and implement real-time embedded AI systems, fostering innovation and technical proficiency.

Graduates, job seekers, business professionals, and optionally researchers or consultants.

Basic knowledge of programming and familiarity with embedded systems concepts.