One Bit

One-bit quantization, the reduction of data to a single binary value, is a crucial technique for efficient data processing and storage, particularly in resource-constrained environments like edge devices. Current research focuses on improving the accuracy of one-bit systems in various applications, including channel estimation (using generative models like Gaussian mixtures), classification (leveraging regularized regression and sparsity), and image processing (optimizing deep learning models for ultra-low-bit operations). These advancements are significant because they enable the deployment of complex algorithms on low-power devices while mitigating the information loss inherent in extreme quantization, leading to improvements in speed and energy efficiency across diverse fields.