FPGA and GPU Utilization in Industrial Image Processing: Comparative Study and Application
DOI:
https://doi.org/10.35870/ijsecs.v5i1.3273Keywords:
FPGA, GPU, Industrial Image Processing, Parallel Computing, CUDA, Performance Optimization, LabVIEWAbstract
This work aims to investigate the FPGA (Field-Programmable Gate Array) and GPU (Graphical Processing Unit) technology in image optimization research for an industrial frontier study. Using an experimental method, the research compared the efficiency of two technologies as implemented in some many image processing algorithms. NI CompactRIO platform for FPGA implementation and NVIDIA GeForce GTX 970 in GPU processing performed differently. As is well known, low-lag applications (camera synchronization, real-time data processing etc.) were very well suited for FPGAs. GPUs with architecture CUDA, on the other hand could be a thousand times faster than traditional CPUs in parallel data processing. Other challenges identified through analysis were FPGA design optimization and GPU resource wise utilization. The results give recommendation in terms of selecting technologies based on the features for image industrial processing applications
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