Model Compression

Report on Current Developments in Model Compression

General Direction of the Field

The field of model compression is rapidly evolving, driven by the need to deploy large-scale models on resource-constrained devices while maintaining performance and efficiency. Recent advancements are focusing on innovative techniques that not only reduce model size and computational demands but also ensure minimal impact on accuracy. The general direction of the field is towards developing unified frameworks that integrate multiple compression methods, leveraging theoretical foundations such as ergodic theory, tropical geometry, and convex optimization to achieve superior results.

One of the key trends is the shift from post-hoc retraining to methods that allow for compression without the need for additional training. This approach not only reduces the time and computational costs associated with compression but also broadens the applicability of these techniques to a wider range of models and devices. Additionally, there is a growing emphasis on system-level optimizations that enhance the efficiency of model inference, making large language models (LLMs) more accessible for real-world applications.

Emerging methodologies, such as hyper-compression and tropical geometry-based approaches, are pushing the boundaries of what is possible in model compression. These methods are designed to handle the complexities of large models, offering high compression ratios with minimal performance degradation. The integration of these advanced techniques into unified frameworks is expected to further advance the field, enabling more efficient and scalable deployment of AI models.

Noteworthy Papers

  • Hyper-Compression: Model Compression via Hyperfunction: Introduces a novel approach using ergodic theory to represent model parameters, achieving high compression ratios without retraining.

  • TropNNC: Structured Neural Network Compression Using Tropical Geometry: Utilizes tropical geometry for structured pruning, demonstrating state-of-the-art performance in compressing linear layers.

Sources

Hyper-Compression: Model Compression via Hyperfunction

Edge AI: Evaluation of Model Compression Techniques for Convolutional Neural Networks

Contemporary Model Compression on Large Language Models Inference

Foundations of Large Language Model Compression -- Part 1: Weight Quantization

Unified Framework for Neural Network Compression via Decomposition and Optimal Rank Selection

TropNNC: Structured Neural Network Compression Using Tropical Geometry