AI in Real-Time Embedded Systems Fundamentals Training by Tonex
This course provides a comprehensive introduction to integrating AI into real-time embedded systems for critical applications. Learn about the fundamentals of AI, embedded systems, and how to design solutions that meet the demands of high safety, low latency, and stringent power constraints. Discover AI’s role in autonomous vehicles, industrial robotics, and medical devices, addressing the unique challenges of real-time processing in embedded environments.
Learning Objectives
By the end of this course, participants will be able to:
- Understand the fundamentals of AI and embedded systems.
- Analyze requirements for AI in real-time, safety-critical applications.
- Identify key challenges in latency, safety, and power consumption.
- Develop AI solutions optimized for embedded environments.
- Apply machine learning models to embedded hardware.
- Evaluate and improve system performance under real-time constraints.
Target Audience
This course is designed for:
- Embedded systems engineers and designers
- Software and hardware engineers in critical applications
- Professionals in AI for autonomous systems, robotics, and medical devices
- R&D professionals working on real-time AI solutions
- Technical managers overseeing AI in embedded systems
- Anyone interested in AI’s role in embedded, real-time environments
Course Outline:
Topic 1: Introduction to Real-Time Embedded Systems and AI
- Overview of embedded systems in real-time applications
- Basics of artificial intelligence and machine learning
- Real-time processing: requirements and constraints
- Safety and reliability considerations
- Embedded AI applications in critical fields
- Current trends in AI-powered embedded systems
Topic 2: Hardware for AI in Embedded Systems
- Selection of hardware for embedded AI applications
- Low-power microcontrollers and AI processors
- GPU and TPU for edge AI applications
- Memory and storage limitations
- Real-time data acquisition and processing
- Hardware optimization techniques
Topic 3: AI Models and Algorithms for Embedded Systems
- Selection of AI models for real-time applications
- Lightweight neural networks and model compression
- Machine learning vs. deep learning in embedded AI
- Decision trees, SVM, and rule-based models
- Reinforcement learning in embedded systems
- Model accuracy vs. performance trade-offs
Topic 4: Real-Time AI Software Architecture
- Architecture design for real-time AI systems
- Firmware and software considerations for embedded AI
- Scheduling and latency management
- Resource allocation and optimization
- Parallel processing and edge computing strategies
- Balancing responsiveness with computation loads
Topic 5: Safety, Security, and Reliability
- Safety standards in critical applications (ISO 26262, IEC 61508)
- Risk assessment and fault-tolerant design
- Security in AI-enabled embedded systems
- Monitoring and diagnostics in real-time systems
- Cybersecurity challenges and best practices
- Validation and verification of AI models
Topic 6: Practical Applications and Case Studies
- AI in autonomous vehicles: object detection, path planning
- Robotics: vision-based control and navigation
- Healthcare devices: monitoring and diagnosis
- Energy-efficient solutions in embedded AI
- Case studies in industrial automation
- Future trends and innovations in AI-driven embedded systems
Elevate your understanding of AI in embedded systems! Join Tonex’s AI in Real-Time Embedded Systems Fundamentals course to gain critical skills and insights into creating high-performance, reliable AI solutions. Register now to stay ahead in this evolving field.