Quantization Error

Quantization error arises from representing continuous-valued data (e.g., neural network weights and activations) using a limited number of bits, impacting model accuracy and efficiency. Current research focuses on mitigating this error in large language models (LLMs) and vision transformers (ViTs), employing techniques like post-training quantization, quantization-aware training, and the development of novel quantization algorithms (e.g., those incorporating learned rotations or adaptive clipping). Reducing quantization error is crucial for deploying large models on resource-constrained devices, improving energy efficiency, and enabling wider accessibility of advanced AI applications.