[Sphinx-Documentation] Refer from network preparation to new section about mem mode

docs/finn/internals.rst

@@ -138,6 +138,8 @@ 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 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`.
 
+.. _mem_mode:
+
 StreamingFCLayer *mem_mode*
 ===========================
 

docs/finn/nw_prep.rst

@@ -37,7 +37,7 @@ After this transformation the ONNX model is streamlined and contains now custom
 Convert to HLS Layers
 =====================
 
-Pairs of binary XNORPopcountMatMul layers are converted to StreamingFCLayers and following Multithreshold layers are absorbed into the MVTU. The result is a model consisting of a mixture of HLS and non-HLS layers. For more details, see :py:mod:`finn.transformation.fpgadataflow.convert_to_hls_layers`.
+Pairs of binary XNORPopcountMatMul layers are converted to StreamingFCLayers and following Multithreshold layers are absorbed into the Matrix-Vector-Activate-Unit (MVAU). The result is a model consisting of a mixture of HLS and non-HLS layers. For more details, see :py:mod:`finn.transformation.fpgadataflow.convert_to_hls_layers`. The MVAU can be implemented in two different modes, *const* and *decoupled*, see chapter :ref:`mem_mode`.
 
 Dataflow Partitioning
 =====================