Edge AI Revolution

Edge AI runs ML models directly on devices (cameras, drones, smartphones, IoT sensors) instead of cloud. Benefits: <50ms latency, privacy (data stays local), works offline, 70-90% lower bandwidth costs.

Edge AI Use Cases

1. Smart Manufacturing

• Visual quality inspection on production lines
• Predictive maintenance with sensor data
• Worker safety monitoring
• <20ms inference for real-time decisions

2. Autonomous Vehicles

• Object detection and tracking
• Lane detection and traffic sign recognition
• Sensor fusion (camera, LiDAR, radar)
• <100ms end-to-end latency requirement

3. Smart Cities & Surveillance

• Traffic monitoring and optimization
• Crowd analytics and anomaly detection
• Parking space detection
• Privacy-preserving (face blurring at edge)

4. Healthcare Wearables

• Real-time health monitoring
• Fall detection and alerts
• ECG abnormality detection
• Low power consumption (days of battery)

Edge Hardware Options

High Performance

• NVIDIA Jetson (Orin, Xavier, Nano): 5-275 TOPS, $100-$2K
• Google Coral: 4 TOPS, $60-150, TensorFlow Lite optimized
• Intel Movidius: VPU for computer vision

Ultra-Low Power

• ARM Cortex-M: Microcontrollers for TinyML
• ESP32: $5-10, WiFi/BLE, for simple models
• MAX78000: Hardware CNN accelerator, <1mW

Model Optimization Techniques

1. Quantization

• INT8 quantization: 4x smaller, 2-4x faster, <1% accuracy loss
• INT4/INT2 for extreme edge
• Post-training quantization (no retraining needed)
• Quantization-aware training (better accuracy)

2. Model Pruning

• Remove 50-90% of parameters with minimal accuracy loss
• Structured pruning for hardware efficiency
• Iterative pruning and fine-tuning

3. Knowledge Distillation

• Train small "student" model from large "teacher"
• 10-100x smaller with 5-10% accuracy retention
• MobileNet, EfficientNet architectures

4. Neural Architecture Search (NAS)

• Automated design of efficient architectures
• Hardware-aware NAS for target device
• EfficientNet, MobileNetV3, EfficientDet

Deployment Pipeline

1. Model Training: Cloud GPUs (PyTorch/TensorFlow)
2. Optimization: Quantization, pruning (TensorRT, ONNX)
3. Conversion: TensorFlow Lite, ONNX Runtime, OpenVINO
4. Testing: Benchmark latency, accuracy on target device
5. OTA Updates: Remote model updates via IoT platform

Case Study: Smart Retail

• Application: People counting and heatmap generation in 50 stores
• Hardware: Jetson Nano ($100) + IP camera per store
• Model: YOLOv8-nano quantized to INT8
• Performance: 30 FPS, 15ms inference, 97% accuracy
• Savings: ₹40L/year vs cloud processing (bandwidth + compute)

Pricing

• Model Optimization: ₹5-15L (per model)
• Edge Deployment: ₹10-30L (pipeline + integration)
• Hardware: ₹5K-2L per device (depending on performance)
• Timeline: 6-12 weeks for full deployment