Quantization Noise

Quantization noise arises from representing continuous neural network weights and activations using a limited number of bits, impacting model accuracy and efficiency. Current research focuses on mitigating this noise in various deep learning architectures, including diffusion models and sequence-to-sequence models, through techniques like quantization-aware training, post-training quantization with noise correction schemes, and mixed-precision quantization. These efforts aim to improve the performance and energy efficiency of deployed models, particularly in resource-constrained environments, by balancing the trade-off between model accuracy and computational cost. The resulting advancements are crucial for deploying large-scale models on edge devices and improving the scalability of AI applications.