Metadata-Version: 2.4
Name: sigqnn
Version: 0.0.1.dev0
Summary: Quantized Neural Networks for Signal Processing (PLACEHOLDER - NOT FUNCTIONAL)
Author-email: Joshua Rothe <jrothe1@alumni.jh.edu>
License: MIT
Project-URL: Homepage, https://github.com/rothej/sigqnn
Project-URL: Repository, https://github.com/rothej/sigqnn
Project-URL: Issues, https://github.com/rothej/sigqnn/issues
Project-URL: Documentation, https://github.com/rothej/sigqnn/blob/main/README.md
Keywords: radio,modulation,classification,quantization,FPGA,FINN,CNN,wireless,signal-processing
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: System :: Hardware
Requires-Python: >=3.12
Description-Content-Type: text/markdown
License-File: LICENSE
Dynamic: license-file

# SigQNN - Signal Processing with Quantized Neural Networks

** DEVELOPMENT IN PROGRESS - NOT YET FUNCTIONAL **

This PyPI package name is reserved for an upcoming open-source toolkit for:
- Automatic Modulation Classification (AMC) with quantized CNNs.
- FPGA deployment via FINN/QONNX  .
- RadioML dataset integration.

**Current Status:** Pre-alpha placeholder. **Do not install.**

**Expected Release:** Q3 2026

**Repository:** https://github.com/rothej/sigqnn

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## About the Project

SigQNN is being developed from research on efficient neural networks for wireless signal classification. The toolkit will provide:

- Pre-configured CNN architectures (VGG-like, more?).
- Quantization-aware training (Brevitas integration).
- Structured pruning for FPGA synthesis.
- RadioML 2016.10a dataset loader with Zenodo mirror.
- ONNX/QONNX export for FINN compiler.

Based on:
- Rothe, J. (2024). *Quantization and Pruning of Convolutional Neural Networks for Efficient FPGA Implementation of Digital Modulation Detection Firmware* [Master's Thesis]
- Dataset: O'Shea & West (2016), RadioML 2016.10a (CC BY-NC-SA 4.0)

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This placeholder ensures the package name is reserved during active development.
