Real-Time Systems & Embedded AI: Intelligent Decision-Making at the Edge
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Course Modules:
Module 1: Introduction to Real-Time Systems
What defines a real-time system?
Hard vs. soft real-time constraints
Key challenges in timing, scheduling, and reliability
Module 2: Fundamentals of Embedded AI
What is embedded AI?
Differences between cloud AI, edge AI, and micro-AI
Applications in robotics, automotive, medical, and consumer devices
Module 3: Hardware Platforms for Embedded AI
Overview: Arduino, Raspberry Pi, ESP32, NVIDIA Jetson
Accelerators: Coral Edge TPU, Intel Movidius, STM32 AI
Sensors, GPIO, and interfacing components
Module 4: Deploying AI Models to Embedded Devices
Model compression: pruning, quantization, and distillation
Tools: TensorFlow Lite, TinyML, Edge Impulse, PyTorch Mobile
Real-world case study: image or audio classification on a microcontroller
Module 5: Real-Time AI in Action
Integrating AI inference with control loops
Scheduling and task prioritization in real-time operating systems (RTOS)
Handling latency, jitter, and failure recovery
Module 6: Capstone Project – Build an Embedded AI Application
Choose a use case (e.g., gesture recognition, smart light controller, voice command AI)
Deploy a lightweight ML model on hardware
Submit performance benchmarks, timing analysis, and video demo
Tools & Technologies Used:
Microcontrollers (Arduino, ESP32, STM32)
Raspberry Pi / NVIDIA Jetson Nano
TensorFlow Lite / TinyML / Edge Impulse
FreeRTOS or Zephyr (optional for real-time execution)
Target Audience:
Embedded systems engineers
Robotics and AI students
IoT developers building smart devices
Innovators applying ML to edge hardware
Global Learning Benefits:
Build low-power, real-time AI systems
Learn to optimize ML models for edge deployment
Combine control systems with machine learning
Develop practical skills for the future of AI-powered devices
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