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: None specified in transcript.
• Duration: 00:03:04

Introduction

This video explores model compression techniques in AI, focusing on pruning, quantization, and knowledge distillation to optimize AI models for efficient deployment on resource-constrained devices. The goal is to reduce model size and improve speed while maintaining accuracy.

Key Takeaways

• Pruning: Eliminates redundant connections by identifying insignificant weights, improving efficiency. Various approaches like magnitude pruning, sensitivity analysis, learned pruning, and structured pruning are discussed.
• Quantization: Reduces precision using different data types (FP32, FP16, int8, binary, ternary) to minimize model size and enhance 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, improving the student model's performance. This involves training a teacher model, generating soft targets, and training the student model using these 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 Onyx runtime are helpful.
• Applications & Validation: Model compression benefits mobile AI applications (face recognition, voice assistance, etc.) and edge computing. Validation involves assessing accuracy, inference speed, memory footprint, and energy consumption.