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Related Concept Videos

Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Statgraphics is a comprehensive statistical software suite designed for both basic and advanced data analysis. Originating in 1980 at Princeton University under Dr. Neil W. Polhemus, it was one of the pioneering tools for statistical computing on personal computers, with its public release in 1982 marking an early milestone in data science software. Over the years, it has evolved into a robust platform for data science, offering tools for regression analysis, ANOVA, multivariate statistics,...

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Related Experiment Video

Updated: May 11, 2026

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
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A Review of Recent Hardware and Software Advances in GPU-Accelerated Edge-Computing Single-Board Computers (SBCs) for

Umair Iqbal1, Tim Davies1, Pascal Perez2

  • 1SMART Infrastructure Facility, University of Wollongong, Wollongong, NSW 2522, Australia.

Sensors (Basel, Switzerland)
|August 10, 2024
PubMed
Summary
This summary is machine-generated.

This review explores GPU-accelerated Single-Board Computers (SBCs) and software for edge computing. It details advancements in computer vision (CV) on SBCs, addressing challenges for smart city applications.

Keywords:
Internet of things (IoT)computer vision (CV)edge computinggraphical processing unit (GPU)single-board computers (SBCs)smart cities

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Edge Computing

Background:

  • Computer Vision (CV) is crucial for Single-Board Computers (SBCs) in smart city applications.
  • Deploying CV on SBCs faces challenges like limited computation, energy efficiency, and real-time processing.
  • Existing research focuses on GPU acceleration and software advancements for SBC performance.

Purpose of the Study:

  • To provide a comprehensive review of GPU-accelerated edge-computing SBCs and software advancements.
  • To analyze recent developments in algorithm optimization, packages, and frameworks for CV on SBCs.
  • To guide AI researchers in selecting optimal hardware-software combinations for their use cases.

Main Methods:

  • Literature review of recent advancements in GPU-accelerated SBCs for edge computing.
  • Detailed overview of software developments including algorithm optimization and deployment packages.
  • Subjective comparative analysis of SBCs based on critical performance factors.

Main Results:

  • Identified key GPU-accelerated SBCs and software solutions for edge AI.
  • Detailed various algorithm optimization techniques and development frameworks.
  • Provided a comparative analysis to aid in the selection of suitable SBCs and software.

Conclusions:

  • GPU acceleration and software optimization are critical for advancing CV on SBCs.
  • Further research is needed to address limitations in current SBCs for edge AI.
  • The review offers insights into the state-of-the-art and future directions for CV on SBCs.