---

name: hls4ml description: "Convert, tune, and deploy hls4ml FPGA inference projects from Keras, PyTorch, and ONNX inputs." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

hls4ml

Use this skill for hls4ml model conversion, backend project generation, tuning, and extension workflows.

Install

• Base package: pip install hls4ml
• Frontend smokes and most CPU inspection tasks: add hls4ml[testing]
• Keras 2 / QKeras / HGQ workflows: add hls4ml[testing-keras2]
• Profiling and plotting: add hls4ml[profiling]
• Distributed arithmetic: add hls4ml[da]
• SNN helpers: add hls4ml[snn]
• Symbolic regression helpers: add hls4ml[sr]

Read references/install-and-environment.md if you need to choose between Keras 2 and Keras 3 families or if the optimization extra needs a separate Python version.

Quick import check:

python -c "import hls4ml; print(hls4ml.__version__)"

For a richer read-only probe, run scripts/check_install.py.

Route map

• frontends — Keras, PyTorch, ONNX, quantized, spiking, serialization, and conversion smoke checks.
• backends — backend selection, generated projects, build/report flows, and report parsing.
• analysis — profiling, precision inference, bit-exact propagation, and resource tuning.
• extensions — custom layers, optimizer passes, template hooks, and backend/writer plugins.

Read first when checking freshness

• references/repo-provenance.md
• references/troubleshooting.md

Shared inspection helper

• scripts/check_install.py — print the installed package version, backend registry, supported-layer counts, and optional dependency status.