Add requested citations to README (#615)

* Update README.md

* update docs

* update

* typo

CITATION.cff

@@ -0,0 +1,23 @@
+cff-version: 1.2.0
+message: "Please cite the following works when using this software."
+type: software
+authors:
+- given-names: "FastML Team"
+title: "hls4ml"
+doi: 10.5281/zenodo.1201549
+repository-code: "https://github.com/fastmachinelearning/hls4ml"
+url: "https://fastmachinelearning.org/hls4ml"
+keywords:
+  - python
+  - machine-learning
+  - FPGA
+  - physics
+  - tensorflow
+  - pytorch
+  - onnx
+  - qonnx
+license: "Apache-2.0"
+abstract: |
+  hls4ml is an open-source software-hardware codesign workflow 
+  to interpret and translate machine learning algorithms for 
+  implementations in hardware, including FPGAs and ASICs.

README.md

@@ -56,3 +56,77 @@ hls_model.build()
 #Print out the report if you want
 hls4ml.report.read_vivado_report('my-hls-test')
 ```
+
+# Citation
+If you use this software in a publication, please cite the software
+```bibtex
+@software{vloncar_2021_5680908,
+  author       = {{FastML Team}},
+  title        = {fastmachinelearning/hls4ml},
+  year         = 2021,
+  publisher    = {Zenodo},
+  doi          = {10.5281/zenodo.1201549},
+  url          = {https://github.com/fastmachinelearning/hls4ml}
+}
+```
+and first publication:
+```bibtex
+@article{Duarte:2018ite,
+    author = "Duarte, Javier and others",
+    title = "{Fast inference of deep neural networks in FPGAs for particle physics}",
+    eprint = "1804.06913",
+    archivePrefix = "arXiv",
+    primaryClass = "physics.ins-det",
+    reportNumber = "FERMILAB-PUB-18-089-E",
+    doi = "10.1088/1748-0221/13/07/P07027",
+    journal = "JINST",
+    volume = "13",
+    number = "07",
+    pages = "P07027",
+    year = "2018"
+}
+```
+Additionally, if you use specific features developed in later papers, please cite those as well. For example, CNNs:
+```bibtex
+@article{Aarrestad:2021zos,
+    author = "Aarrestad, Thea and others",
+    title = "{Fast convolutional neural networks on FPGAs with hls4ml}",
+    eprint = "2101.05108",
+    archivePrefix = "arXiv",
+    primaryClass = "cs.LG",
+    reportNumber = "FERMILAB-PUB-21-130-SCD",
+    doi = "10.1088/2632-2153/ac0ea1",
+    journal = "Mach. Learn. Sci. Tech.",
+    volume = "2",
+    number = "4",
+    pages = "045015",
+    year = "2021"
+}
+@article{Ghielmetti:2022ndm,
+    author = "Ghielmetti, Nicol\`{o} and others",
+    title = "{Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml}",
+    eprint = "2205.07690",
+    archivePrefix = "arXiv",
+    primaryClass = "cs.CV",
+    reportNumber = "FERMILAB-PUB-22-435-PPD",
+    doi = "10.1088/2632-2153/ac9cb5",
+    journal ="Mach. Learn. Sci. Tech.",
+    year = "2022"
+}
+```
+binary/ternary networks:
+```bibtex
+@article{Loncar:2020hqp,
+    author = "Ngadiuba, Jennifer and others",
+    title = "{Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML}",
+    eprint = "2003.06308",
+    archivePrefix = "arXiv",
+    primaryClass = "cs.LG",
+    reportNumber = "FERMILAB-PUB-20-167-PPD-SCD",
+    doi = "10.1088/2632-2153/aba042",
+    journal = "Mach. Learn. Sci. Tech.",
+    volume = "2",
+    pages = "015001",
+    year = "2021"
+}
+```

docs/index.rst

@@ -47,3 +47,85 @@ Tutorials
 Detailed tutorials on how to use ``hls4ml``'s various functionalities can be found at:
 
 https://github.com/fastmachinelearning/hls4ml-tutorial
+
+Citation
+=================================
+If you use this software in a publication, please cite the software
+
+..  code-block:: bibtex
+
+    @software{vloncar_2021_5680908,
+    author       = {{FastML Team}},
+    title        = {fastmachinelearning/hls4ml},
+    year         = 2021,
+    publisher    = {Zenodo},
+    doi          = {10.5281/zenodo.1201549},
+    url          = {https://github.com/fastmachinelearning/hls4ml}
+    }
+
+and first publication:
+
+..  code-block:: bibtex
+    
+    @article{Duarte:2018ite,
+        author = "Duarte, Javier and others",
+        title = "{Fast inference of deep neural networks in FPGAs for particle physics}",
+        eprint = "1804.06913",
+        archivePrefix = "arXiv",
+        primaryClass = "physics.ins-det",
+        reportNumber = "FERMILAB-PUB-18-089-E",
+        doi = "10.1088/1748-0221/13/07/P07027",
+        journal = "JINST",
+        volume = "13",
+        number = "07",
+        pages = "P07027",
+        year = "2018"
+    }
+
+Additionally, if you use specific features developed in later papers, please cite those as well. For example, CNNs:
+
+..  code-block:: bibtex
+
+    @article{Aarrestad:2021zos,
+        author = "Aarrestad, Thea and others",
+        title = "{Fast convolutional neural networks on FPGAs with hls4ml}",
+        eprint = "2101.05108",
+        archivePrefix = "arXiv",
+        primaryClass = "cs.LG",
+        reportNumber = "FERMILAB-PUB-21-130-SCD",
+        doi = "10.1088/2632-2153/ac0ea1",
+        journal = "Mach. Learn. Sci. Tech.",
+        volume = "2",
+        number = "4",
+        pages = "045015",
+        year = "2021"
+    }
+    @article{Ghielmetti:2022ndm,
+        author = "Ghielmetti, Nicol\`{o} and others",
+        title = "{Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml}",
+        eprint = "2205.07690",
+        archivePrefix = "arXiv",
+        primaryClass = "cs.CV",
+        reportNumber = "FERMILAB-PUB-22-435-PPD",
+        doi = "10.1088/2632-2153/ac9cb5",
+        journal ="Mach. Learn. Sci. Tech.",
+        year = "2022"
+    }
+
+binary/ternary networks:
+
+..  code-block:: bibtex
+
+    @article{Loncar:2020hqp,
+        author = "Ngadiuba, Jennifer and others",
+        title = "{Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML}",
+        eprint = "2003.06308",
+        archivePrefix = "arXiv",
+        primaryClass = "cs.LG",
+        reportNumber = "FERMILAB-PUB-20-167-PPD-SCD",
+        doi = "10.1088/2632-2153/aba042",
+        journal = "Mach. Learn. Sci. Tech.",
+        volume = "2",
+        pages = "015001",
+        year = "2021"
+    }