# fastmachinelearning/hls4ml

Machine learning on FPGAs using HLS

Repository: https://github.com/fastmachinelearning/hls4ml
Canonical: https://ross.abutalabs.com/products/hls4ml
Homepage: https://fastmachinelearning.org/hls4ml
Language: Python
License: Apache-2.0
License Family: permissive
Topics: hls, machine-learning, fpga, python, keras, pytorch, onnx, vivado, vivado-hls, neural-network, intel-hls
Last push: 2026-08-26T11:38:58+00:00

## Health v2 (maintenance only)
Score: 82/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 51, longevity 100
- inputs: {"age_days": 3234, "days_push": 7, "days_rel": 166, "gap_med": 136, "n_releases_24m": 4}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2114, forks 582 (observed 2026-08-28T04:06:15.814776+00:00)

## What it is
hls4ml is a Python package that converts machine learning models from Keras, PyTorch, and ONNX into high-level synthesis (C++) code for FPGA firmware. It targets ultra-low-latency, low-power neural network inference, originally for LHC trigger systems but now used across scientific and industrial domains.

## Use cases
- deploy neural networks on FPGAs with microsecond latency
- convert Keras or PyTorch models to HLS firmware
- build low-latency trigger systems for particle physics detectors
- run quantized neural network inference on low-power hardware
- implement ML inference for satellite or biomedical signal processing
- compile ONNX models to FPGA bitstreams

## When to choose
- you need ultra-low-latency (microsecond-scale) ML inference on FPGAs
- your model is small and can be compressed with quantization and pruning
- you use supported architectures like MLPs, CNNs, or RNNs with Keras, PyTorch, or ONNX
- you work with AMD/Xilinx, Intel, or Catapult HLS toolchains

## When to avoid
- you need large models like transformers or graph networks, which are not yet stable
- you target Windows or macOS, which are not supported
- you don't have access to a vendor HLS toolchain
- you need GPU or CPU inference rather than FPGA deployment

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, compiler, llm-inference, embedded
- domain: machine-learning, embedded-systems, hardware, deep-learning, gpu-computing
- platform: python, embedded
- tags: fpga, high-level-synthesis, neural-network-inference, low-latency, quantization, vivado, vitis, catapult, keras, pytorch, onnx, qkeras, high-energy-physics, linux

## Member repositories
- fastmachinelearning/hls4ml (main) score 82

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:15.814776+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 2026-08-30T02:53:28.953058+00:00, confidence not recorded.
  - readme: https://github.com/fastmachinelearning/hls4ml (fetched 2026-08-28T04:06:15.814776+00:00, sha 05f30b61df37)
  - homepage: https://fastmachinelearning.org/hls4ml (fetched 2026-08-29T10:33:30.066958+00:00, sha 751d98b8affa)
  - site_page: https://fastmachinelearning.org/hls4ml/intro/status.html (fetched 2026-08-29T10:33:30.076071+00:00, sha eaf3c705f837)
  - site_page: https://fastmachinelearning.org/hls4ml/intro/faq.html (fetched 2026-08-29T10:33:30.078006+00:00, sha aca849c5c85c)
- Data as of 2026-08-30T08:39:29.467469+00:00.