Develop and apply model compression techniques including pruning, quantization, and knowledge distil

About this Video

• Video Title: Develop and apply model compression techniques including pruning, quantization, and knowledge distil
• Channel: Gooru Content
• Speakers: Not specified in transcript
• Duration: 00:03:04

Introduction

This video explores model compression techniques for optimizing AI models, focusing on pruning, quantization, and knowledge distillation to improve efficiency and deployment on resource-constrained devices. The goal is to balance model size and performance while preserving accuracy.

Key Takeaways

• Pruning: Eliminates redundant weights and connections in a model to reduce size, improving speed. Different approaches include magnitude pruning, sensitivity analysis, learned pruning, and structured pruning.
• Quantization: Reduces precision of model weights (e.g., using FP16, INT8), minimizing size and improving efficiency. Methods include post-training quantization, quantization-aware training, dynamic quantization, and calibration-based quantization.
• Knowledge Distillation: Transfers knowledge from a large, high-accuracy "teacher" model to a smaller, more efficient "student" model. This involves training the teacher, generating soft targets, and training the student to learn from those outputs.
• Compression Pipeline: Involves training a base model, applying distillation, implementing pruning, performing quantization, and fine-tuning for optimal results. Tools like TensorFlow Model Optimization, PyTorch Quantization, and ONNX Runtime can assist.
• Validation: Assess accuracy, inference speed, memory footprint, and energy consumption to ensure effective model compression. Benchmarks are available for models like ResNet, BERT, MobileNet, and YOLOv5.