{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/11"}],"enrichment":{"capability":"QONNX provides Python utilities to work with quantized neural networks in ONNX format, including custom operators for arbitrary-precision integer and minifloat quantization, model execution, shape inference, and inference cost analysis.","skillfed_tags":["quantization","neural-networks","onnx"],"use_cases":["Export quantized models from Brevitas or QKeras and verify their behavior by executing them with QONNX utilities","Analyze inference cost of a quantized model in terms of bit operations, memory footprint, and per-layer MAC counts","Convert quantized models from QONNX representation to QCDQ-style quantization for different deployment targets","Develop custom quantization transformations and executable operators using QONNX's Python infrastructure","Prepare quantized neural networks for FPGA deployment via FINN or hls4ml by using QONNX as an intermediate representation"],"what_it_does":"QONNX is a toolkit for representing and working with quantized neural networks in ONNX format. It introduces custom operators\u2014IntQuant, FloatQuant, BipolarQuant, and Trunc\u2014that enable arbitrary-precision integer and minifloat quantization while remaining compatible with standard ONNX protobuf. Quantized values are stored as standard floats to maintain compatibility.\n\nThe package provides utilities for executing QONNX models for functional verification, performing shape inference and constant folding, calculating inference costs in terms of mixed-precision MACs and memory volume, and transforming models between quantization representations. It integrates with quantization-aware training frameworks like Brevitas, QKeras, and HAWQ for export, and supports deployment via FPGA-focused frameworks like FINN and hls4ml.","worth_installing":"Yes, if you work with quantized neural networks and need a common representation for export, analysis, or deployment. The package is actively maintained, has no known vulnerabilities, uses permissive licensing, and integrates with established QAT frameworks and FPGA toolchains. Install friction is low. Not necessary if you only work with standard full-precision ONNX models."},"id":"qonnx","links":{"html":"https://skillfed.io/packages/qonnx","md":"https://skillfed.io/packages/qonnx.md","pypi":"https://pypi.org/project/qonnx/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-02-19","license_spdx":null,"license_treatment":"permissive","name":"qonnx","python_support":"unspecified","summary":"Frontend and utilities for QONNX"},"popularity":{"monthly_downloads":74027,"position":14888,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.0.0"}