State of the Art Quantization

Neural network quantization aims to reduce computational cost and memory footprint by representing network parameters and activations with lower precision (e.g., 8-bit or even 1-bit integers) without significant accuracy loss. Current research focuses on developing advanced quantization techniques, including mixed-precision quantization (adapting bit-width per layer) and novel quantization-aware training methods, often applied to resource-intensive architectures like Vision Transformers (ViTs) and convolutional neural networks (CNNs). These efforts are crucial for deploying deep learning models on resource-constrained edge devices and accelerating inference speed, impacting both scientific research and real-world applications across various domains. The development of automated quantization frameworks further streamlines the process and improves accessibility.