research-article

Towards Fine-Grained Online Adaptive Approximation Control for Dense SLAM on Embedded GPUs

Authors:
Tiancong Bu

College of Communication Engineering, Jilin University, Changchun, China

College of Communication Engineering, Jilin University, Changchun, China
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,
Kaige Yan

College of Communication Engineering, Jilin University, Changchun, China

College of Communication Engineering, Jilin University, Changchun, China
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,
Jingweijia Tan

College of Computer Science and Technology, Jilin University, Changchun, China

College of Computer Science and Technology, Jilin University, Changchun, China
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ACM Transactions on Design Automation of Electronic Systems Volume 27 Issue 2Article No.: 11pp 1–19https://doi.org/10.1145/3486612

Published:02 November 2021Publication History

ACM Transactions on Design Automation of Electronic Systems

Abstract

Dense SLAM is an important application on an embedded environment. However, embedded platforms usually fail to provide enough computation resources for high-accuracy real-time dense SLAM, even with high-parallelism architecture such as GPUs. To tackle this problem, one solution is to design proper approximation techniques for dense SLAM on embedded GPUs. In this work, we propose two novel approximation techniques, critical data identification and redundant branch elimination. We also analyze the error characteristics of the other two techniques—loop skipping and thread approximation. Then, we propose SLaPP, an online adaptive approximation controller, which aims to control the error to be under an acceptable threshold. The evaluation shows SLaPP can achieve 2.0× performance speedup and 30% energy saving on average compared to the case without approximation.

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Index Terms

Towards Fine-Grained Online Adaptive Approximation Control for Dense SLAM on Embedded GPUs
1. Computer systems organization
  1. Architectures
    1. Parallel architectures
      1. Single instruction, multiple data
  2. Embedded and cyber-physical systems
    1. Embedded systems
      1. Embedded software

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Published in
ACM Transactions on Design Automation of Electronic Systems Volume 27, Issue 2
March 2022
217 pages
ISSN:1084-4309
EISSN:1557-7309
DOI:10.1145/3494074
Editor:
X. Sharon Hu
University of Notre Dame, USA
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
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ACM Journals for the Design of Smart and Connected Systems
Publication History
- Published: 2 November 2021
- Accepted: 1 August 2021
- Revised: 1 June 2021
- Received: 1 February 2021
Published in todaes Volume 27, Issue 2

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Author Tags
Approximate computing
dense SLAM
embedded GPUs
online adaptive control
Qualifiers
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Towards Fine-Grained Online Adaptive Approximation Control for Dense SLAM on Embedded GPUs

ACM Transactions on Design Automation of Electronic Systems

Abstract

REFERENCES

Cited By

Index Terms

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SFU-Driven Transparent Approximation Acceleration on GPUs

Efficient Convex Optimization on GPUs for Embedded Model Predictive Control

Fine-Grained Synchronizations and Dataflow Programming on GPUs