Integer Quantization

Integer quantization aims to reduce the memory and computational requirements of deep learning models by representing their weights and activations using a limited number of integer bits, thereby improving efficiency for deployment on resource-constrained devices. Current research focuses on developing novel quantization algorithms, particularly for large language models (LLMs) and vision transformers (ViTs), often employing techniques like post-training quantization and mixed-precision approaches to minimize accuracy loss. These advancements are crucial for enabling the deployment of powerful deep learning models on mobile and embedded systems, expanding the accessibility and applicability of AI across various domains.