Low Rank Quantization

Low-rank quantization aims to reduce the computational cost and memory footprint of large neural networks, particularly large language models (LLMs) and convolutional neural networks, by representing their weight matrices with lower-rank approximations and quantizing their values to lower bit precisions. Current research focuses on developing efficient algorithms for low-rank decomposition and quantization, often incorporating techniques like Hadamard transforms and optimized matrix factorization methods, applied to both training and post-training scenarios. This approach offers significant potential for deploying large models on resource-constrained devices and accelerating training and inference speeds, impacting both the efficiency of machine learning research and the practical deployment of AI applications.