Data Multiplexing

Data multiplexing is a technique that combines multiple data inputs into a single composite input for simultaneous processing, aiming to improve efficiency and throughput in various applications. Current research focuses on developing efficient multiplexing and demultiplexing methods, particularly within deep neural networks (DNNs) and large language models (LLMs), often employing novel architectures like reversible multiplexers or hybrid approaches combining multiplexing with model compression or caching strategies. This approach holds significant promise for accelerating inference in resource-constrained environments, such as edge devices and high-throughput systems, and for enhancing the performance of computationally intensive tasks in fields like natural language processing and image processing.