Master Embedded AI for Smart Devices
Learn to deploy machine learning models on microcontrollers, edge devices, and IoT systems. Get hands-on with TensorFlow Lite, Edge Impulse, and real embedded hardware through VTU-recognized training.
Embedded AI Internship
Master the fusion of AI with embedded systems to build intelligent edge devices, IoT solutions, and real-time AI applications on hardware platforms.
Program Overview
This comprehensive Embedded AI internship bridges the gap between artificial intelligence and hardware systems. You’ll learn to deploy neural networks on microcontrollers, build real-time AI applications, and create intelligent IoT devices using cutting-edge embedded platforms and optimization techniques.
Edge AI Deployment
Deploy AI models on microcontrollers and embedded systems for real-time inference
Hardware-AI Integration
Integrate sensors, actuators, and AI algorithms for intelligent embedded systems
Real-time Processing
Build low-latency AI applications optimized for embedded platforms
IoT Intelligence
Create smart IoT devices with on-device AI capabilities and cloud connectivity
Detailed Curriculum
Embedded Systems Fundamentals
- Microcontroller architecture (ARM, AVR)
- C/C++ programming for embedded systems
- GPIO, Timers, and Interrupts
- Communication protocols: I2C, SPI, UART
- Sensor integration and interfacing
- RTOS (Real-Time Operating Systems) basics
AI/ML for Embedded Systems
- Fundamentals of Machine Learning for embedded
- TinyML concepts and frameworks
- Neural network compression techniques
- Quantization and pruning for edge devices
- Model optimization for low-power devices
- Transfer learning for edge AI
Edge AI Platforms & Frameworks
- TensorFlow Lite for Microcontrollers
- PyTorch Mobile and Edge AI deployment
- OpenMV for computer vision on microcontrollers
- Edge Impulse platform for TinyML
- Coral Edge TPU and Google AIY kits
- NVIDIA Jetson platform
- ARM CMSIS-NN library
Computer Vision on Edge
- Image processing on embedded systems
- Object detection on edge devices
- Face recognition on microcontrollers
- Gesture recognition systems
- Optimized CNN architectures for edge
- Camera interfacing and image capture
- Real-time video processing
IoT & Wireless Communication
- Wireless protocols: BLE, WiFi, LoRa, Zigbee
- MQTT and CoAP for IoT communication
- Cloud connectivity for embedded AI
- Edge-cloud collaboration architectures
- Power management for battery-powered AI
- Security in embedded AI systems
- OTA (Over-the-Air) updates for AI models
Advanced Projects & Deployment
- End-to-end embedded AI project development
- Hardware prototyping and PCB design basics
- Performance benchmarking and optimization
- Power consumption analysis and optimization
- Production deployment strategies
- Testing and validation of embedded AI systems
- VTU format report submission
Tools & Technologies You’ll Master
Innovative Projects You’ll Build
Smart Home AI Assistant
Build an embedded AI assistant with voice recognition and home automation control using ESP32 and TensorFlow Lite.
Edge Vision Security System
Create a real-time face recognition security system on Raspberry Pi with OpenCV and deep learning models.
Gesture Control Drone
Develop a drone that can be controlled through hand gestures using computer vision on embedded hardware.
Smart Agriculture IoT System
Build an intelligent agriculture system with soil analysis, crop monitoring, and automated irrigation using edge AI.
Health Monitoring Wearable
Create a wearable device with health monitoring and anomaly detection using TinyML on microcontrollers.
Autonomous Edge Robot
Develop an autonomous robot with obstacle avoidance and path planning using computer vision on edge devices.
Ready to Build Intelligent Hardware?
Join our Embedded AI internship and master the cutting-edge fusion of AI with hardware systems
Contact Us: intern@krugna.com | +91 89700 02233