Low Bit

Low-bit quantization aims to reduce the computational and memory demands of deep neural networks by representing model parameters and activations using fewer bits, thereby improving efficiency without significant accuracy loss. Current research focuses on developing novel quantization techniques for various architectures, including transformers, convolutional neural networks, and large language models, often employing methods like data-free quantization, layer-wise quantization, and adaptive precision strategies. This area is crucial for deploying large models on resource-constrained devices and accelerating inference, impacting both the efficiency of machine learning research and the practical applications of AI in various domains.