Pimp the docs a bit

Author: vloncar

docs/advanced/model_optimization.rst

@@ -130,5 +130,5 @@ Note, to ensure DSPs are optimized, "unrolled" Dense multiplication must be used
 .. code-block:: Python
 
     hls_config = config_from_keras_model(optimized_model)
-    hls_config['Model']['DenseResourceImplementation'] = 'Unrolled'
-    # Any addition hls4ml config, such as strategy, reuse factor etc...
+    hls_config['Model']['Strategy'] = 'Unrolled'
+    # Any addition hls4ml config, reuse factor etc...

docs/api/configuration.rst

@@ -103,7 +103,10 @@ For Vivado backend the options are:
 * **IOType**\ : your options are ``io_parallel`` or ``io_stream`` which defines the type of data structure used for inputs, intermediate activations between layers, and outputs. For ``io_parallel``, arrays are used that, in principle, can be fully unrolled and are typically implemented in RAMs. For ``io_stream``, HLS streams are used, which are a more efficient/scalable mechanism to represent data that are produced and consumed in a sequential manner. Typically, HLS streams are implemented with FIFOs instead of RAMs. For more information see `here <https://docs.xilinx.com/r/en-US/ug1399-vitis-hls/pragma-HLS-stream>`__.
 * **HLSConfig**\: the detailed configuration of precision and parallelism, including:
   * **ReuseFactor**\ : in the case that you are pipelining, this defines the pipeline interval or initiation interval
-  * **Strategy**\ : Optimization strategy on FPGA, either "Latency" or "Resource". If none is supplied then hl4ml uses "Latency" as default. Note that a reuse factor larger than 1 should be specified when using "resource" strategy. An example of using larger reuse factor can be found `here. <https://github.com/fastmachinelearning/models/tree/master/keras/KERAS_dense>`__
+  * **ParallelizationFactor**\ : The number of output "pixels" to compute in parallel in convolutional layers. Increasing this parameter results in significant increase in resources required on the FPGA.
+  * **Strategy**\ : Optimization strategy on FPGA, either "Latency", "Resource" or "Unrolled". If none is supplied then hl4ml uses "Latency" as default. Note that a reuse factor larger than 1 should be specified when using "resource" or "unrolled" strategy. An example of using larger reuse factor can be found `here. <https://github.com/fastmachinelearning/models/tree/master/keras/KERAS_dense>`__
+  * **PipelineStyle**\ : Set the top level pipeline style. Valid options are "auto", "pipeline" and "dataflow". If unspecified, it defaults to "auto".
+  * **PipelineInterval**\ : Optionally override the desired initiation interval of the design. Only valid in combination with "pipeline" style. If unspecified, it is left to the compiler to decide, ideally matching the largest reuse factor of the network.
   * **Precision**\ : this defines the precsion of your inputs, outputs, weights and biases. It is denoted by ``ap_fixed<X,Y>``\ , where ``Y`` is the number of bits representing the signed number above the binary point (i.e. the integer part), and ``X`` is the total number of bits.
   Additionally, integers in fixed precision data type (\ ``ap_int<N>``\ , where ``N`` is a bit-size from 1 to 1024) can also be used. You have a chance to further configure this more finely with per-layer configuration described below.