halalboro/fpga-accelerators
Hardware Accelerators on FPGA for Computer Vision Applications
This project helps embedded systems and machine learning enthusiasts learn how to deploy neural network hardware accelerators on FPGAs. It guides you from training a neural network on a standard computer to detecting objects in real-time using video feeds on an FPGA. The intended user is anyone exploring real-time computer vision on embedded hardware, especially those new to Xilinx Vitis-AI.
Use this if you are an embedded systems developer or ML enthusiast looking to accelerate computer vision models like YOLO for real-time object detection on FPGA platforms like Xilinx Ultra96v2 or KV260.
Not ideal if you are looking for an actively maintained, ready-to-use software library or a high-level solution that doesn't require deep engagement with FPGA development workflows.
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12
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3
Language
C++
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Last pushed
Dec 16, 2025
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