Mixed Quantization

Mixed quantization optimizes deep learning model efficiency by assigning different bit-precisions to various model components (weights, activations, or even individual layers). Current research focuses on developing algorithms that automatically determine optimal quantization strategies, often leveraging techniques like Fisher information or differentiable search to adapt precision to layer sensitivity or task-critical aspects. This approach significantly reduces model size and computational cost, improving inference speed and energy efficiency for various applications, including computer vision, natural language processing, and mobile deployment of large models.