# Xilinx/finn

Dataflow compiler for QNN inference on FPGAs

Repository: https://github.com/Xilinx/finn
Canonical: https://ross.abutalabs.com/products/finn
Homepage: https://xilinx.github.io/finn
Language: Python
License: BSD-3-Clause
License Family: permissive
Topics: dataflow, quantization, fpga, compiler, neural-network
Last push: 2026-08-31T18:19:57+00:00

## Health v2 (maintenance only)
Score: 68/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 100, release rhythm 8, longevity 100
- inputs: {"age_days": 2987, "days_push": 2, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1046, forks 306 (observed 2026-09-01T02:13:58.278607+00:00)

## What it is
FINN is an open-source dataflow compiler from AMD/Xilinx that generates highly efficient FPGA accelerators for quantized neural network (QNN) inference. It transforms trained QNNs into customized streaming dataflow architectures with sub-microsecond latency and high throughput.

## Use cases
- compile quantized neural networks to FPGA accelerators
- build ultra-low-latency DNN inference on FPGAs
- generate dataflow architectures for QNNs
- explore quantization and parallelization design space for hardware
- deploy neural networks on Xilinx FPGAs
- research hardware/software co-design for neural network inference

## When to choose
- you need sub-microsecond latency or very high throughput inference on FPGAs
- your network is a quantized neural network (e.g., BNN, QNN from Brevitas)
- you want an open-source, customizable FPGA DNN compiler
- you're doing research across the hardware/software abstraction stack

## When to avoid
- you need generic DNN acceleration for arbitrary float models
- you don't have access to Xilinx FPGA hardware or Vitis toolchain
- you want a plug-and-play deployment without design space exploration
- you can't use Docker, as the compiler only supports Docker-based execution

## Facets
- artifact type: framework
- maturity: active
- function: compiler, machine-learning, llm-inference, deep-learning
- domain: machine-learning, deep-learning, embedded-systems, hardware, gpu-computing
- platform: python
- tags: fpga, quantized-neural-networks, dataflow-architecture, hls, vitis, hardware-acceleration, qnn, amd-xilinx, linux, docker

## Member repositories
- Xilinx/finn (main) score 68

## Provenance
- Observed fields: from GitHub, fetched 2026-09-01T02:13:58.278607+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:02:11.487595+00:00, confidence not recorded.
  - readme: https://github.com/Xilinx/finn (fetched 2026-09-01T02:13:58.278607+00:00, sha a9331d16e2bd)
  - homepage: https://xilinx.github.io/finn (fetched 2026-08-29T13:03:24.756689+00:00, sha 73a7839cf8ad)
  - registry_pypi: https://pypi.org/pypi/finn/json (fetched 2026-08-29T13:03:24.765592+00:00, sha 8aec26602b3e)
- Data as of 2026-08-30T08:39:29.467469+00:00.