Finn-examples

This release is functionally identical with FINN-examples v0.0.7 but softens the constraint on the numpy version, to allow for installation on Pynq-Z1 and Pynq-Z2 with 32-bit ARM processor.

Updates to FINN-examples since the last release include
- New build flows and bitfiles for all models with FINN v0.10+
- DSP-packed MVAU in MobileNet-v1 on ZCU104 and VGG10-RadioML example
- Efficient implementation for per tensor thresholding, e.g. for ResNet-50 which allows the full design to be implemented on-chip without external memory
- New example for Pynq-Z1: Binarized CNV topology from the [FINN paper](https://arxiv.org/abs/1612.07119) trained on the [German Traffic Sign Recognition Benchmark (GTSRB) dataset](https://benchmark.ini.rub.de/gtsrb_news.html)
- More stable flow for ResNet-50, fixed floorplanning and fixed FIFO depths to allow for faster rebuild times
- The opset versions of the model files were updated to allow easy integration with the verification capabilities of the builder flow

This pre-release updates the existing build flows to be compatible with FINN v0.10 (https://github.com/Xilinx/finn/discussions/1026) and v0.10.1 (https://github.com/Xilinx/finn/discussions/1128). Additionally, updated onnx files of the existing examples are published to be referenced in the final release.

Updates to FINN-examples since the last release include
- Migration to PYNQ v3.0.1 and FINN v0.9
- Newly built bitfiles with FINN v0.9
- Migrated to new FINN (v0.9) driver: updated notebooks & QONNX dependency
- A [new notebook](https://github.com/Xilinx/finn-examples/blob/main/finn_examples/notebooks/6_cybersecurity_with_mlp.ipynb) example and [build script](https://github.com/Xilinx/finn-examples/tree/main/build/cybersecurity-mlp):
- **Cybersecurity MLP**: classifying network packets as malicious or not with an MLP on the UNSW-NB15 dataset.
- Minor fixes in reporting and documentation

Known issues
- The build flow for ResNet-50 for the Alveo U250 has known issues and we're currently working on resolving them. However, you can still execute the associated notebook, as we provide a pre-built FPGA bitfile generated with an older Vivado (/FINN) version targeting the [xilinx_u250_xdma_201830_2](https://www.xilinx.com/products/boards-and-kits/alveo/package-files-archive/u250-2018-3-1.html) platform.
- The build flow for MobileNet-v1 for the ZCU104 works, but there are currently (known) issues with runtime-writeable weights. Therefore, we have excluded this example, but you can still integrate this IP into your design and try to deploy it with your driver.

* No changes to prebuilt examples
* Update FINN to v0.8.1 for rebuilds
* Update driver and scripts to account for QONNX migration (finn-base is no longer a dependency)
* Minor fixes in reporting and documentation

Three new examples:

* **[Face mask wear and positioning:](https://github.com/Xilinx/finn-examples/blob/main/finn_examples/notebooks/3_binarycop_mask_detection.ipynb)** Low-power BNN classifier for Pynq-Z1 for correct face mask wear and positioning. Contributed by TU Munich and BMW.
* **[Radio signal modulation:](https://github.com/Xilinx/finn-examples/blob/main/finn_examples/notebooks/5_radioml_with_cnns.ipynb)** Classify RadioML 2018.1 at 250k inferences/second on a ZCU104. See also our [ITU challenge](https://bit.ly/brevitas-radioml-challenge-21) on the topic. Contributed by Felix Jentzsch.
* **[Keyword spotting:](https://github.com/Xilinx/finn-examples/blob/main/finn_examples/notebooks/4_keyword_spotting.ipynb)** Classify spoken commands from the Google Speech Commands dataset at 250k inferences/second on a Pynq-Z1. Utilizes the new QONNX build flow. Contributed by Hendrik Borras.


radioml
RadioML 2018.1 example

kws
Data files and models for the upcoming keyword spotting (KWS) example

binary-cop
New contribution from TU Munich / BMW: Binary Neural Network-based COVID-19 Face-Mask Wear and Positioning Predictor on Edge Devices
See https://github.com/NaelF/BinaryCoP/tree/master/notebook for more details
Note: currently PYNQ-Z1 only, bitfile only