Data Free Quantization

Data-free quantization aims to compress deep neural networks for faster inference without using the original training data, addressing privacy and security concerns. Current research focuses on developing data-free quantization methods for various architectures, including vision transformers and large language models, often employing techniques like generative models to synthesize representative data or leveraging internal model statistics (e.g., batch normalization parameters) for quantization parameter calibration. This field is significant because it enables efficient deployment of large models on resource-constrained devices while protecting sensitive data, impacting both the development of more accessible AI and the responsible use of machine learning.