doonny/PipeCNN

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1386 stars · 373 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

PipeCNN is an OpenCL-based FPGA accelerator for large-scale convolutional neural network inference, written in C with pipelined kernels. It supports both Intel OpenCL SDK and Xilinx Vitis flows and includes a ModelZoo with pre-quantized VGG-16 and ResNet-50 models.

Use cases

• accelerate cnn inference on fpga hardware
• run vgg-16 or resnet-50 image classification on an fpga board
• benchmark quantized deep neural networks on intel arria-10 or xilinx u50 cards
• learn opencl-based high-level synthesis for deep learning accelerators
• measure top-1/top-5 accuracy of cnns using fpga acceleration

When to choose

• you have a supported Intel or Xilinx FPGA board and want OpenCL-based CNN inference
• you need a scalable, resource-configurable hardware accelerator for research or education
• you want to experiment with quantized CNN models on FPGAs

When to avoid

• you need GPU or CPU inference without FPGA hardware
• you require training support rather than inference
• you need support for modern CNN architectures beyond VGG-16 and ResNet-50
• you want an actively developed project with frequent updates

Facets

library · maturity maintenance

machine-learning deep-learning llm-inference deep-learning hardware computer-vision artificial-intelligence cross-platform opencl fpga-accelerator hls cnn-inference altera xilinx-vitis hardware-acceleration quantization fpga

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For agents

markdown · JSON · MCP: product_card(name="doonny/PipeCNN")