In this paper, we introduce data multiplexing (DataMUX), a technique that
enables deep neural networks to process multiple inputs simultaneously using a
single compact representation. DataMUX demonstrates that neural networks are
capable of generating accurate predictions over mixtures of inputs, resulting
in increased throughput with minimal extra memory requirements. Our approach
uses two key components -- 1) a multiplexing layer that performs a fixed linear
transformation to each input before combining them to create a mixed
representation of the same size as a single input, which is then processed by
the base network, and 2) a demultiplexing layer that converts the base
network's output back into independent representations before producing
predictions for each input. We show the viability of DataMUX for different
architectures (Transformers, and to a lesser extent MLPs and CNNs) across six
different tasks spanning sentence classification, named entity recognition and
image classification. For instance, DataMUX for Transformers can multiplex up
to $20$x/$40$x inputs, achieving $11$x/$18$x increase in throughput with
minimal absolute performance drops of $<2\%$ and $<4\%$ respectively on MNLI, a
natural language inference task. We also provide a theoretical construction for
multiplexing in self-attention networks and analyze the effect of various
design elements in DataMUX.

本文介绍了 DataMUX 技术，通过多路复用层和解复用层，深度神经网络可同时处理多个输入，减少内存需求，具有较高吞吐量，可适用于不同的神经网络结构和任务。