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Xilinx · Jan 31, 2024 · 6478948 · 6478948
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diff --git a/docs/about.md b/docs/about.md
@@ -1,4 +1,4 @@
-## Who are we?
+# Who are we?
 
 The FINN team consists of members of AMD Research under Ralph Wittig (AMD Research & Advanced Development) and members of Custom & Strategic Engineering under Allen Chen, working very closely with the Pynq team.
 

diff --git a/docs/events.md b/docs/events.md
@@ -1,4 +1,4 @@
-## Events, Tutorials and Keynotes
+# Events, Tutorials and Keynotes
 * [FINN tutorial at FPL’23 (4th September 2023)](https://github.com/Xilinx/finn/discussions/861)
 * [FINN tutorial at FPL’22 (2nd September 2022)](https://github.com/Xilinx/finn/discussions/672)
 * [FINN tutorial, March 2021 (24th March 2021)](https://xilinx.github.io/finn//2021/03/11/finn-tutorial-march21.html)

diff --git a/docs/img/SICK_Logo.jpg b/docs/img/SICK_Logo.jpg
diff --git a/docs/index.md b/docs/index.md
@@ -12,10 +12,8 @@ The FINN compiler is under active development <a href="https://github.com/Xilinx
 <br>
 ## Features
 
-* **Templated Vitis HLS library of streaming components:** FINN comes with an
-HLS hardware library that implements convolutional, fully-connected, pooling and
-LSTM layer types as streaming components. The library uses C++ templates to
-support a wide range of precisions.
+* **Templated Vitis HLS and RTL library of streaming components:** FINN comes with a
+library of HLS and RTL modules that implement neural network layers as streaming components.
 * **Ultra low-latency and high performance
 with dataflow:** By composing streaming components for each layer, FINN can
 generate accelerators that can classify images at sub-microsecond latency.
@@ -27,15 +25,10 @@ separate compute resources per layer, either automatically or manually, and
 generating the full design for synthesis. This enables rapid exploration of the
 design space.
 
-## Who are we?
+## Customer testimonials
+<img src="img/SICK_Logo.jpg" alt="drawing" width="400"/>
+*“The FINN toolset is showing huge potential using it in upcoming SICK products.
+It is easy to use and with an extraordinary performance and very promising results. 
+In the future, flexible implementations of ML in our products with FINN can be a great advantage and even replace static architectures as they are currently used.
+Thanks to the FINN team for the great cooperation”*
 
-The FINN team consists of members of AMD Research under Ralph Wittig (AMD Research & Advanced Development) and members of Custom & Strategic Engineering under Allen Chen, working very closely with the Pynq team.
-
-<img src="img/finn-team.png" alt="The FINN Team (AMD Research and Advanced Development)" width="400"/>
-
-From top left to bottom right: Yaman Umuroglu, Michaela Blott, Alessandro Pappalardo, Lucian Petrica, Nicholas Fraser,
-Thomas Preusser, Jakoba Petri-Koenig, Ken O’Brien
-
-<img src="img/finn-team1.png" alt="The FINN Team (Custom & Strategic Engineering)" width="400"/>
-
-From top left to bottom right: Eamonn Dunbar, Kasper Feurer, Aziz Bahri, John Monks, Mirza Mrahorovic
diff --git a/docs/quickstart.md b/docs/quickstart.md
@@ -1,9 +1,9 @@
-## Quickstart
+# Quickstart
 
 <img align="left" src="img/finn-stack.PNG" alt="drawing" style="margin-right: 20px" width="300"/>
 
 
-### Repo links
+## Repo links
 Depending on what you would like to do, we have different suggestions on where to get started:
 
 * **I want to try out prebuilt QNN accelerators on my FPGA board.** Head over to [finn-examples](https://github.com/Xilinx/finn-examples)
@@ -14,13 +14,13 @@ our PyTorch library for quantization-aware training.
 * **I want to understand how it all fits together.** Check out the <a href="https://github.com/Xilinx/finn">FINN compiler</a>.
 
 
-### Introduction videos & articles
+## Introduction videos & articles
 * [Video tutorial @FPGA 2021](https://www.youtube.com/watch?v=zw2aG4PhzmA)
 * [FINN paper](https://arxiv.org/pdf/1612.07119.pdf)
 * [FINN-R paper](https://arxiv.org/pdf/1809.04570.pdf) - this is what the current tool-flow is mostly based on
 * Brevitas tutorial @TVMCon2021: [Video](https://www.youtube.com/watch?v=wsXx3Hr5kZs) and [Jupyter notebook](https://github.com/Xilinx/brevitas/blob/master/notebooks/Brevitas_TVMCon2021.ipynb)
 
-### More in depth material
+## More in depth material
 * [FINN documentation](https://finn.readthedocs.io/en/latest/)
 * [Brevitas documentation](https://xilinx.github.io/brevitas/)
 * [FINN installation instructions](https://finn.readthedocs.io/en/latest/getting_started.html)