finn
A dataflow compiler for quantized neural network inference on FPGAs, generating highly efficient custom accelerators.
What is finn?
FINN is a dataflow compiler framework for quantized neural network inference on FPGAs. It generates highly efficient, customized dataflow-style architectures to accelerate neural network inference, achieving high throughput and low latency. The framework is open-source and experimental, developed by AMD Research & Advanced Development to explore neural network implementations across software/hardware stacks.
Researchers and engineers working on FPGA-based deep learning acceleration, particularly those focused on quantized neural networks and custom hardware architectures. It's also suitable for developers exploring high-performance inference solutions with low latency requirements.
Developers choose FINN for its ability to generate highly efficient, dataflow-style FPGA accelerators tailored to specific quantized neural networks, offering superior performance and flexibility compared to generic inference frameworks. Its open-source nature enables deep customization and research across the hardware/software stack.
Overview
Dataflow compiler for QNN inference on FPGAs
Use Cases
Best For
- Accelerating quantized neural network inference on FPGAs
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