Bit Quantization

Bit quantization aims to reduce the memory and computational requirements of large neural networks by representing their weights and activations using fewer bits, thereby accelerating inference and enabling deployment on resource-constrained devices. Current research focuses on developing effective quantization techniques for various architectures, including large language models (LLMs), diffusion models, and convolutional neural networks, often employing methods like post-training quantization (PTQ) and quantization-aware training (QAT) to minimize accuracy loss. Success in this area is crucial for making advanced deep learning models more accessible and energy-efficient, impacting both scientific research and practical applications across diverse fields.