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Image Processing Hardware Acceleration-A Review of Operations Involved and Current Hardware Approaches
Costin-Emanuel Vasile1, Andrei-Alexandru Ulmămei1, Călin Bîră1
1Department of Electronic Devices, Circuits and Architectures, National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania.
Journal of Imaging
|December 27, 2024
Summary
This review analyzes hardware acceleration for image processing and neural network inference. It covers CPUs, GPUs, ASICs, FPGAs, and low-power devices for efficient computation.
Area of Science:
- Computer Engineering
- Artificial Intelligence
- Image Processing
Background:
- Hardware acceleration is crucial for efficient image processing and neural network inference.
- Traditional architectures face challenges with increasing computational demands.
Purpose of the Study:
- To provide an in-depth analysis of current hardware acceleration techniques.
- To examine hardware platforms for image processing and neural network deployment.
- To consider solutions for low-power, resource-constrained devices.
Main Methods:
- Review of existing literature on hardware acceleration.
- Analysis of key operations in image processing and neural network inference.
- Comparison of CPU-GPU systems, ASICs, and FPGAs.
Main Results:
- Detailed examination of various hardware acceleration strategies.
- Evaluation of performance and efficiency trade-offs across different platforms.
- Identification of trends in hardware design for AI and image processing.
Conclusions:
- Hardware acceleration is essential for optimizing image processing and neural network tasks.
- Diverse hardware solutions exist, each with specific advantages.
- Future research should focus on energy-efficient designs for edge computing.
