Merge pull request #55 documentation updates

DOCS: Fixed links to TF docs + added link to online MLI documentation…

Author: JaccovG

README.md

@@ -1,8 +1,8 @@
 embARC Machine Learning Inference Library
 ==================================================
 
-This repository contains source code of embARC Machine Learning Inference Library (embARC MLI Library),
-examples and documentation.
+This repository contains source code of embARC Machine Learning Inference Library (embARC MLI Library), 
+documentation and examples. Read the documentation at [embarc.org](https://embarc.org/embarc_mli).
 
 ## Release notes
 ----------------

doc/documents/MLI_kernels/convolution_2d.rst

@@ -49,7 +49,7 @@ inputs shape.
 
 For more details on calculations see convolution part of `TensorFlow–Neural Network details`_. 
 
-.. _TensorFlow–Neural Network details: https://www.tensorflow.org/api_guides/python/nn.
+.. _TensorFlow–Neural Network details: https://www.tensorflow.org/versions/r1.11/api_guides/python/nn
 
 ReLU activation function might be applied to result of convolution. The
 following types of ReLU activations are supported (for more info see

doc/documents/MLI_kernels/pooling_max.rst

@@ -34,7 +34,7 @@ padding parameters. This logic is similar to convolution 2D operation
 For more information on calculations, see the pooling part of
 `TensorFlow–Neural Network details`_.
 
-.. _TensorFlow–Neural Network details: https://www.tensorflow.org/api_guides/python/nn   
+.. _TensorFlow–Neural Network details: https://www.tensorflow.org/versions/r1.11/api_guides/python/nn   
 
 .. caution::
    Ensure that input and output

doc/documents/library_model/functions.rst

@@ -137,8 +137,8 @@ Naming convention for the specializations: \
    |                       | parameters to achieve     |                       |
    |                       | same output size          |                       |
    |                       | (similar to ‘SAME’        |                       |
-   |                       | padding scheme used       |                       |
-   |                       | in TensorFlow [3])        |                       |
+   |                       | `padding scheme`_ used    |                       |
+   |                       | in TensorFlow)            |                       |
    +-----------------------+---------------------------+-----------------------+
    | ``Input channels``    | [_ch\ *n*]                | convolution group,    |
    |                       |                           | pooling group         |
@@ -182,6 +182,9 @@ Naming convention for the specializations: \
    |                       | specializations.          |                       |
    +-----------------------+---------------------------+-----------------------+
 
+.. _padding scheme: https://www.tensorflow.org/versions/r1.11/api_guides/python/nn#Notes_on_SAME_Convolution_Padding
+
+
 
 For example, the function name of a 16bit 2d convolution kernel with
 CHW layout and a kernel size of 3x3 and stride of 1 is:

doc/documents/library_model/hw_dependencies_config.rst

@@ -109,7 +109,7 @@ round to the nearest even). All parameters are described in *MetaWare
 Fixed-Point Reference for ARC EM and ARC HS*.
 
 .. note::
-   The MLI Library sets the required DSP mode inside each function where it is needed, but does not restore it to previous state. If another ARC DSP code beside MLI library is used in an application, ensure that you set the required DSP mode before its execution. For more information see  “Configuring the ARC DSP Extensions” section of *MetaWare DSP Programming Guide for ARC EM and ARC HS* or “Using the FXAPI” section of entry [5] of *MetaWare Fixed-Point Reference for ARC EM and ARC HS*.
+   The MLI Library sets the required DSP mode inside each function where it is needed, but does not restore it to previous state. If another ARC DSP code beside MLI library is used in an application, ensure that you set the required DSP mode before its execution. For more information see  “Configuring the ARC DSP Extensions” section of *MetaWare DSP Programming Guide for ARC EM and ARC HS* or “Using the FXAPI” section of *MetaWare Fixed-Point Reference for ARC EM and ARC HS*.
 
 AGU Support
 ^^^^^^^^^^^

examples/example_cifar10_caffe/README.md

@@ -106,6 +106,21 @@ More Options on Building and Running
 ---------------------------------------
 CIFAR-10 example application is implemented in the same way as LSTM Based HAR example and provides the same configuration and running abilities. For more details see appropriate HAR example [description part](/examples/example_har_smartphone/README.md#more-options-on-building-and-running).
 
+Data Memory Requirements
+----------------------------
+
+Example application uses statically allocated memory for model weights and intermediate results (activations) and structures. Requirements for them depends on model bit depth 
+configuration define and listed in table below. Before compiling application for desired hardware configuration, be sure it has enough memory to keep data.
+
+|                      Data                              |   MODEL_BIT_DEPTH=8   |  MODEL_BIT_DEPTH=816  |  MODEL_BIT_DEPTH=16  |
+| :----------------------------------------------------: | :-------------------: | :-------------------: | :------------------: |
+| Weights <br/>*.mli_model* and *mli_model_p2 * sections |  33212 bytes          | 33212 bytes           | 66420 bytes          |
+| Activations 1 <br/>*.Zdata * section                   |  32768 bytes          | 65536 bytes           | 65536 bytes          |
+| Activations 2 <br/>*.Ydata * section                   |  8192 bytes           | 16384 bytes           | 16384 bytes          |
+| Structures <br/>*.mli_data* section                    |  384 bytes            | 384 bytes             | 384 bytes            |
+
+By default, application uses MODEL_BIT_DEPTH=16 mode. Application code size depends on target hardware configuration and compilation flags. MLI Library code is wrapped into mli_lib section.
+
 References
 ----------------------------
 CIFAR-10 Dataset:

examples/example_har_smartphone/README.md

@@ -120,6 +120,10 @@ For this reason you can build and check application with 8 and 16 bit depth of N
 
        gmake TCF_FILE=../../hw/em9d.tcf EXT_CFLAGS="-DMODEL_BIT_DEPTH=8"
 
+* 8x16: 8 bit depth of coefficients and 16 bit depth of data:
+
+       gmake TCF_FILE=../../hw/em9d.tcf EXT_CFLAGS="-DMODEL_BIT_DEPTH=816"
+
 Example application may be used in three modes:
 1. **Built-in input processing.** Uses only hard-coded vector for the single input model inference. 
 No application input arguments.
@@ -139,6 +143,22 @@ Input test-set and labels paths are required as argument.
 Notes: If the example is compiled with GNU tools, then these modes are transferred to the application using mdb_com_gnu command script file for MetaWare Debugger.
        Modify this file to customize the example run mode.
 
+
+Data Memory Requirements
+----------------------------
+
+Example application uses statically allocated memory for model weights and intermediate results (activations) and structures. Requirements for them depends on model bit depth 
+configuration define and listed in table below. Before compiling application for desired hardware configuration, be sure it has enough memory to keep data.
+
+|                      Data                         |   MODEL_BIT_DEPTH=8   |  MODEL_BIT_DEPTH=816  |  MODEL_BIT_DEPTH=16  |
+| :-----------------------------------------------: | :-------------------: | :-------------------: | :------------------: |
+| Weights <br/>(*.mli_model* section)              |  17160 bytes          | 17160 bytes           | 34316 bytes          |
+| Activations <br/>(*.Xdata* and *.Ydata* sections) |  8352 bytes           | 16704 bytes           | 16704 bytes          |
+| Structures <br/>(*.mli_data* section)           |  496 bytes            | 496 bytes             | 496 bytes            |
+
+By default, application uses MODEL_BIT_DEPTH=16 mode. Application code size depends on target hardware configuration and compilation flags. MLI Library code is wrapped into mli_lib section.
+
+
 References
 ----------------------------
 GitHub project served as starting point for this example: