# A multitask deep learning model for real-time deployment in embedded systems

Source: [A multitask deep learning model for real-time deployment in embedded systems](<https://www.sota2.com/research/paper/68fa63c01e209131cea00b97>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:49:10.785Z

Authors: Miquel Mart\\'i, Atsuto Maki

Published: 2017-10-31T23:59:23.000Z

## Abstract

We propose an approach to Multitask Learning (MTL) to make deep learning models faster and lighter for applications in which multiple tasks need to be solved simultaneously, which is particularly useful in embedded, real-time systems. We develop a multitask model for both Object Detection and Semantic Segmentation and analyze the challenges that appear during its training. Our multitask network is 1.6x faster, lighter and uses less memory than deploying the single-task models in parallel. We conclude that MTL has the potential to give superior performance in exchange of a more complex training process that introduces challenges not present in single-task models.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1711.00146>)

[PDF](<https://arxiv.org/pdf/1711.00146.pdf>)