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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
Summary
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.
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.
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