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#########
Changelog
#########
1.3.0 [Processor Linux SDK 5.3]
===============================
**Added**
#. Added DSP support for MNIST example.
**Changed**
#. PSDK 5.3 and TIDL-API 1.3 use a slightly modified TIDL network binary
format to support ONNX network import. TIDL-API 1.3, as well as
tidl-viewer utility, can read both network formats before and after the
change. Network binary in TIDL-API 1.2 and earlier is of size 483364
bytes, network binary in TIDL-API 1.3 is of size 484384 bytes.
#. Improved performance of concat layer on C66x DSP.
1.2.2 [Processor Linux SDK 5.2]
===============================
**Added**
#. Updated API implementation to minimize TIDL API/OpenCL dispatch overhead using multiple execution contexts in the :term:`ExecutionObject`.
Refer to :ref:`mnist-example` example for details.
#. Execution Graph generation
Enable a two phase approach to generating execution graphs. Use the
following API function to enable timestamp generation:
.. code:: cpp
bool EnableTimeStamps(const std::string& file = "timestamp.log", size_t num_frames=32);
The generated log file can be viewed by using the execution_graph.py script. Refer to :ref:`execution-graph` for details.
#. Added Python 3 bindings for TIDL API. See the ``examples/pybind`` directory for examples of using the Python bindings. Set PYTHONPATH to the location of ``tidl.so``.
.. code:: bash
root@am57xx-evm:~# export PYTHONPATH=/home/root/tidl-api/tidl_api
root@am57xx-evm:~# python3
>>> import tidl
>>> help (tidl)
**Removed**
#. Configuration::enableInternalInput. Not used by the API.
#. Execution::GetExecutionObjects().
Use Execution::operator[] and Execution::GetNumExecutionObjects() instead.
See :ref:`examples` for usage.
#. The timing methods for host execution in EOPs and EOs:
* GetProcessTimeInMilliSeconds()
* GetHostProcessTimeInMilliSeconds()
These methods were replaced by a timestamp based approach because they were
no longer accurate with multiple ExecutionObject contexts and pipelining.
Refer to :ref:`execution-graph` for details.
1.1.0 [Processor Linux SDK 5.1]
===============================
**Added**
#. :term:`ExecutionObjectPipeline` class to hide complexity of executing network across C66x/EVE
#. API methods for tracing outputs from intermediate network layers - see :ref:`network_layer_output`.
#. Support for updating layer group id assignment before execution - see :ref:`layer-group-override`.
#. Provide feedback to the user on parameter and network heap size requirements - see :ref:`sizing_device_heaps`.
1.0.0 [Processor Linux SDK 5.0]
===============================
First release of the TI Deep Learning API. TIDL API brings deep learning to the edge by enabling applications to leverage TI's proprietary, highly optimized CNN/DNN implementation on the EVE and C66x DSP compute engines. TIDL will initially target Vision/2D use cases.
**Supported AM57x Sitara Processors**
* `AM5749`_ (offload to EVEs and C66x DSPs)
* `AM571x`_ (offload to C66x DSPs)
* `AM5728`_ (offload to C66x DSPs)
* `AM5748`_ (offload to C66x DSPs)
**Supported Evaluation Modules (EVMs)**
* `AM574x IDK EVM`_
* `AM572x EVM`_
* `AM571x IDK EVM`_
.. _AM572x EVM: http://www.ti.com/tool/tmdsevm572x
.. _AM571x IDK EVM: http://www.ti.com/tool/tmdxidk5718
.. _AM574x IDK EVM: http://www.ti.com/tool/tmdsidk574
.. _AM571x: http://www.ti.com/processors/sitara/arm-cortex-a15/am57x/products.html#p2098=1%20C66x&p809=2;2
.. _AM5728: http://www.ti.com/product/AM5728
.. _AM5748: http://www.ti.com/product/am5748
.. _AM5749: http://www.ti.com/product/am5749
.. _AM574x: http://www.ti.com/processors/sitara/arm-cortex-a15/am57x/products.html#p2098=2%20C66x&p815=ECC
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