# Investigations on the inference optimization techniques and their impact on multiple hardware platforms for Semantic Segmentation

Source: [Investigations on the inference optimization techniques and their impact on multiple hardware platforms for Semantic Segmentation](<https://www.sota2.com/research/paper/68fa66b21e209131cea11e46>)

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

Data updated: 2025-10-25T21:56:21.324Z

Authors: Sethu Hareesh Kolluru

Published: 2019-11-29T08:08:28.000Z

## Abstract

In this work, the task of pixel-wise semantic segmentation in the context of self-driving with a goal to reduce the inference time is explored. Fully Convolutional Network (FCN-8s, FCN-16s, and FCN-32s) with a VGG16 encoder architecture and skip connections is trained and validated on the Cityscapes dataset. Numerical investigations are carried out for several inference optimization techniques built into TensorFlow and TensorRT to quantify their impact on the inference time and network size. Finally, the trained network is ported on to an embedded platform (Nvidia Jetson TX1) and the inference time, as well as the total energy consumed for inference across hardware platforms, are compared.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1911.12993>)

[PDF](<https://arxiv.org/pdf/1911.12993.pdf>)