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Introduction
Artificial intelligence has made spectacular progress, thanks to increasingly complex and powerful models driving innovations across industries. However, as models grow larger, they require more computational resources, making deployment on resource-constrained devices challenging.This is a significant hurdle for deploying AI on low-power devices such as smartphones, drones, and IoT devices.
To address this, AI model compression techniques help reduce model size while maintaining performance, making AI more efficient and practical for real-world applications. This article explores the most effective compression techniques, challenges, and real-world use cases.
Some popular AI Compression techniques
Numerous model compression techniques can be used to reduce model size. Here’s an overview of the most popular techniques:
Pruning removes less important parts of a neural network, making it smaller and more efficient. Many deep learning models are over-parameterized, meaning that some parameters contribute little to accuracy. After training, these redundant parameters can be removed with minimal impact.
Pruning: Remove less important parts of a neural network to make it smallerand more efficient. Many deep learning models are over-parameterized, meaning that some parameters contribute little to accuracy. After training, these redundant parameters can be removed with minimal impact.
- There are various types of pruning, including:
- Magnitude Pruning: Eliminates weights with the lowest absolute value
- Structured Pruning: Removes neurons, filters or entire channels
- Unstructured Pruning: Removes individual weights in a more fine-grained manner.
- Quantization: Converts inputs, outputs, weights and/or activations of a model from high-precision representations (e.g. fp32) to lower-precision representations (e.g. fp16, int32, int16, int8, and even int2). Here are a few types of quantization :
- Post-Training Quantization (PTQ): Performs quantization after training
- Quantization-Aware Training (QAT) : Trains the model while simulating quantization for better performance.
- Knowledge Distillation: A smaller model (student) is trained to imitate a larger, more accurate model (teacher). The student model learns to replicate the teacher’s outputs, achieving similar performance with fewer parameters.
- There are different strategies for Knowledge Distillation : online distillation, offline distillation and self-distillation.
- Low-rank factorization: This technique identifies redundant parameters in deep neural networks using matrix and tensor decomposition optimizing architectures such as convolutional networks and transformer-based models.
- Neural Architecture Search: NAS automates the search for model architectures, optimizing them for deployment while reducing unnecessary complexity.
Challenges in model compression
Reducing model size can degrade accuracy and generalizability , making it crucial to balance efficiency and performance. Compressed models may also become less robust to data variations. Each compression technique requires careful calibration—deciding how much to prune, the appropriate level of quantization, or the best distillation strategy—to minimize performance loss while maximizing efficiency.
Conclusion
AI model compression is critical for deploying powerful AI systems in real-world, resource-constrained environments. Techniques like pruning, quantization, and knowledge distillation help reduce size while maintaining performance. However, achieving the right balance between efficiency, accuracy, and ethical considerations remains a challenge.
As AI continues to evolve, advancements in model compression will play a vital role in making AI more accessible, sustainable, and practical for a wide range of industries.