# 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.