DataMUX: Data Multiplexing for Neural Networks

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, re...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2022-11
Main Authors: Murahari, Vishvak, Jimenez, Carlos E, Yang, Runzhe, Narasimhan, Karthik
Format: Article
Language:English
Subjects:
Artificial neural networks
Demultiplexing
Image classification
Linear transformations
Multiplexing
Neural networks
Object recognition
Representations
Transformers
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 \(
ISSN:2331-8422