Edge AI deployment optimization mobile inference

Optimize AI models for edge deployment with mobile inference, model compression, and real-time processing constraints. Model compression techniques: 1. Quantization: FP32 to INT8, post-training quantization, quantization-aware training. 2. Pruning: weight pruning, structured pruning, magnitude-based pruning, gradual sparsification. 3. Knowledge distillation: teacher-student training, soft targets, temperature scaling. Mobile optimization: 1. Model size constraints: <10MB for mobile apps, <100MB for edge devices. 2. Inference optimization: ONNX runtime, TensorFlow Lite, Core ML for iOS deployment. 3. Hardware acceleration: GPU inference, Neural Processing Units (NPU), specialized chips. Deployment frameworks: 1. TensorFlow Lite: mobile/embedded deployment, delegate acceleration, model optimization toolkit. 2. PyTorch Mobile: C++ runtime, operator support, optimization passes. 3. ONNX Runtime: cross-platform inference, hardware-specific optimizations. Real-time constraints: 1. Latency requirements: <100ms for interactive applications, <16ms for real-time video. 2. Memory constraints: RAM usage minimization, model partitioning, streaming inference. 3. Power efficiency: battery optimization, model scheduling, dynamic frequency scaling. Edge computing scenarios: 1. Computer vision: real-time object detection, image classification, pose estimation. 2. Natural language: on-device speech recognition, text classification, language translation. 3. IoT applications: sensor data processing, anomaly detection, predictive maintenance. Performance monitoring: 1. Inference speed: frames per second, latency percentiles, throughput measurement. 2. Accuracy preservation: model accuracy after compression, A/B testing, quality metrics. 3. Resource utilization: CPU/GPU usage, memory consumption, power draw monitoring, thermal management for sustained performance.