Conifer

New Features
- Static accelerators: make bitfiles from `xilinxhls` backend projects https://github.com/thesps/conifer/pull/53
- More utilities for model inspection https://github.com/thesps/conifer/pull/61
- Vivado synthesis option for `xilinxhls` backend for better resource estimation https://github.com/thesps/conifer/pull/61

Bug Fixes
- Don't pad trees automatically when `unroll` is `False` for `xilinxhls` backend (proper support for sparse trees) https://github.com/thesps/conifer/pull/61
- Report reading is more robust in case of missing files https://github.com/thesps/conifer/pull/61

Deprecated Features
- `vivadohls` and `vitishls` backends are no longer generated dynamically. Only `xilinxhls` backend remains. Tool discovery is still in place and both `Vivado HLS` and `Vitis HLS` remain supported. https://github.com/thesps/conifer/pull/61

What's Changed
* Static Accelerators by thesps in https://github.com/thesps/conifer/pull/53
* Miscellaneous Improvements by thesps in https://github.com/thesps/conifer/pull/61
* Updates to boards/accelerators by thesps in https://github.com/thesps/conifer/pull/62


**Full Changelog**: https://github.com/thesps/conifer/compare/v1.3...v1.4-beta.1

New Features
* Python backend by thesps in 55

Bug Fixes
* Fix links in README.md by Chriisbrown in https://github.com/thesps/conifer/pull/59
* CPP backend minor fix by thesps in https://github.com/thesps/conifer/pull/60


**Full Changelog**: https://github.com/thesps/conifer/compare/v1.2...v1.3

New features:

- `Forest Processing Unit` for fast, reconfigurable BDT inference on FPGAs

Bug fixes:
- Fix faulty include on fixed-point emulation for VHDL backend

New features:
- Support for [TensorFlow Decision Forests](https://www.tensorflow.org/decision_forests)
- 'Unrolled' Xilinx HLS optimization for much faster C Synthesis time, enabled by default with `Unroll` configuration parameter (see performance plots on [the PR](https://github.com/thesps/conifer/pull/41))
- Synthesis report reading for HLS and VHDL backends: `conifer_model.read_report()` for models of those backends
- `new_config` parameter of `conifer.model.load_model` to override a saved model's configuration (e.g. to change backend or precision)
- Simulator discovery for VHDL backend (use whichever is installed)
- Model metadata saved with model JSON export for provenance tracking - conifer version, model conversion time
- Documentation webpages at https://ssummers.web.cern.ch/conifer/
- Significantly overhauled internal representation

Bug fixes:
- Fix to `sklearn` converter for newer `sklearn` versions