Quantization Aware

Quantization-aware techniques aim to reduce the computational cost and memory footprint of deep neural networks (DNNs) by representing model parameters and activations with lower precision (e.g., using fewer bits). Current research focuses on developing efficient quantization methods for various DNN architectures, including those used in image super-resolution, object detection, and autonomous driving, often employing mixed-precision strategies and hardware-aware optimization. These advancements are crucial for deploying DNNs on resource-constrained devices like edge computers and mobile platforms, improving energy efficiency and enabling real-time applications. Furthermore, research explores correlated quantization schemes to improve communication efficiency in distributed training settings.