[Sphinx Documentation] Edit brevitas export page and changed notebook links to...

[Sphinx Documentation] Edit brevitas export page and changed notebook links to links to tutorial page

docs/finn/brevitas_export.rst

@@ -10,4 +10,6 @@ Brevitas Export
 
 FINN expects an ONNX model as input. This can be a model trained with `Brevitas <https://github.com/Xilinx/brevitas>`_. Brevitas is a PyTorch library for quantization-aware training and the FINN Docker image comes with several `example Brevitas networks <https://github.com/maltanar/brevitas_cnv_lfc>`_. Brevitas provides an export of a quantized network in ONNX representation. The resulting model consists only of `ONNX standard nodes <https://github.com/onnx/onnx/blob/master/docs/Operators.md>`_, but also contains additional attributes for the ONNX nodes to represent low precision datatypes. To work with the model it is wrapped into :ref:`modelwrapper` provided by FINN. 
 
+At this stage we can already use the functional verification flow to simulate the model using Python, this is marked in the graphic with the dotted arrow. For more details please have look at :ref:`verification`.
+
 The model can now be further processed in FINN, the next flow step is :ref:`nw_prep`.

docs/finn/internals.rst

@@ -5,7 +5,7 @@ Internals
 Intermediate Representation: FINN-ONNX
 ======================================
 
-FINN uses `ONNX <https://github.com/onnx/onnx>`_ as an intermediate representation (IR) for neural networks. As such, almost every component inside FINN uses ONNX and its `Python API <https://github.com/onnx/onnx/blob/master/docs/PythonAPIOverview.md>`_, so you may want to familiarize yourself with how ONNX represents DNNs. Specifically, the `ONNX protobuf description <https://github.com/onnx/onnx/blob/master/onnx/onnx.proto>`_ (or its `human-readable documentation <https://github.com/onnx/onnx/blob/master/docs/IR.md>`_ and the `operator schemas <https://github.com/onnx/onnx/blob/master/docs/Operators.md>`_ are useful as reference documents. We also provide a Jupyter notebook (`1-FINN-HowToWorkWithONNX <https://github.com/Xilinx/finn/blob/dev/notebooks/1-FINN-HowToWorkWithONNX.ipynb>`_) that can help to get familiar with ONNX by showing how to work with a simple ONNX model in FINN.
+FINN uses `ONNX <https://github.com/onnx/onnx>`_ as an intermediate representation (IR) for neural networks. As such, almost every component inside FINN uses ONNX and its `Python API <https://github.com/onnx/onnx/blob/master/docs/PythonAPIOverview.md>`_, so you may want to familiarize yourself with how ONNX represents DNNs. Specifically, the `ONNX protobuf description <https://github.com/onnx/onnx/blob/master/onnx/onnx.proto>`_ (or its `human-readable documentation <https://github.com/onnx/onnx/blob/master/docs/IR.md>`_ and the `operator schemas <https://github.com/onnx/onnx/blob/master/docs/Operators.md>`_ are useful as reference documents. We also provide a Jupyter notebook that can help to get familiar with ONNX by showing how to work with a simple ONNX model in FINN, see chapter :ref:`tutorials` for details.
 
 .. note:: FINN uses ONNX is a specific way that we refer to as FINN-ONNX, and not all ONNX graphs are supported by FINN (and vice versa).
 
@@ -17,7 +17,7 @@ ONNX does not support datatypes smaller than 8-bit integers, whereas in FINN we
 Custom Operations/Nodes
 =======================
 
-FINN uses many custom operations (op_type in ONNX NodeProto) that are not defined in the ONNX operator schema. These custom nodes are marked with domain="finn" in the protobuf to identify them as such. These nodes can represent specific operations that we need for low-bit networks, or operations that are specific to a particular hardware backend. To get more familiar with custom operations and how they are created, please take a look in the Jupyter notebook `7-FINN-CustomOps <https://github.com/Xilinx/finn/blob/dev/notebooks/7-FINN-CustomOps.ipynb>`_ or directly in the module :py:mod:`finn.custom_op`.
+FINN uses many custom operations (op_type in ONNX NodeProto) that are not defined in the ONNX operator schema. These custom nodes are marked with domain="finn" in the protobuf to identify them as such. These nodes can represent specific operations that we need for low-bit networks, or operations that are specific to a particular hardware backend. To get more familiar with custom operations and how they are created, please take a look in the Jupyter notebook about CustomOps (see chapter :ref:`tutorials` for details) or directly in the module :py:mod:`finn.custom_op`.
 
 Custom ONNX Execution Flow
 ==========================
@@ -127,12 +127,12 @@ ModelWrapper contains more useful functions, if you are interested please have a
 Analysis Pass
 =============
 
-An analysis pass traverses the graph structure and produces information about certain properties. It gets the model in the ModelWrapper as input and returns a dictionary of the properties the analysis extracts. If you are interested in how to write an analysis pass for FINN, please take a look at the Jupyter notebook `4-FINN-HowToAnalysisPass <https://github.com/Xilinx/finn/blob/dev/notebooks/4-FINN-HowToAnalysisPass.ipynb>`_. For more details about existing analysis passes in FINN, see module :py:mod:`finn.analysis`.
+An analysis pass traverses the graph structure and produces information about certain properties. It gets the model in the ModelWrapper as input and returns a dictionary of the properties the analysis extracts. If you are interested in how to write an analysis pass for FINN, please take a look at the Jupyter notebook about how to write an analysis pass, see chapter :ref:`tutorials` for details. For more information about existing analysis passes in FINN, see module :py:mod:`finn.analysis`.
 
 .. _transformation_pass:
 
 Transformation Pass
 ===================
 
-A transformation passes changes (transforms) the given model, it gets the model in the ModelWrapper as input and returns the changed model (ModelWrapper) to the FINN flow. Additional the flag *model_was_changed* which indicates if a transformation has to be performed more than once, is returned. If you are interested in how to write a transformation pass for FINN, please take a look at the Jupyter notebook `5-FINN-HowToTransformationPass <https://github.com/Xilinx/finn/blob/dev/notebooks/5-FINN-HowToTransformationPass.ipynb>`_. For more details about existing transformation passes in FINN, see module :py:mod:`finn.transformation`.
+A transformation passes changes (transforms) the given model, it gets the model in the ModelWrapper as input and returns the changed model (ModelWrapper) to the FINN flow. Additional the flag *model_was_changed* which indicates if a transformation has to be performed more than once, is returned. If you are interested in how to write a transformation pass for FINN, please take a look at the Jupyter notebook about how to write a transformation pass, see chapter :ref:`tutorials` for details. For more information about existing transformation passes in FINN, see module :py:mod:`finn.transformation`.
 

docs/finn/tutorials.rst

+.. _tutorials:
+
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 Tutorials
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