Overview of Inference Engine Plugin Library

The plugin architecture of the Inference Engine allows to develop and plug independent inference solutions dedicated to different devices. Physically, a plugin is represented as a dynamic library exporting the single CreatePluginEngine function that allows to create a new plugin instance.

Inference Engine Plugin Library

Inference Engine plugin dynamic library consists of several main components:

1. Plugin class:
   • Provides information about devices of a specific type.
   • Can create an executable network instance which represents a Neural Network backend specific graph structure for a particular device in opposite to the InferenceEngine::ICNNNetwork interface which is backend-independent.
   • Can import an already compiled graph structure from an input stream to an executable network object.
2. Executable Network class:
   • Is an execution configuration compiled for a particular device and takes into account its capabilities.
   • Holds a reference to a particular device and a task executor for this device.
   • Can create several instances of Inference Request.
   • Can export an internal backend specific graph structure to an output stream.
3. Inference Request class:
   • Runs an inference pipeline serially.
   • Can extract performance counters for an inference pipeline execution profiling.
4. Asynchronous Inference Request class:
   • Wraps the Inference Request class and runs pipeline stages in parallel on several task executors based on a device-specific pipeline structure.

NOTE: This documentation is written based on the Template plugin, which demonstrates plugin

development details. Find the complete code of the Template, which is fully compilable and up-to-date, at <dldt source dir>/docs/template_plugin.

Detailed guides

• Build a plugin library using CMake*
• Plugin and its components testing
• Quantized networks
• Writing ngraph transformations guide

API References

• Inference Engine Plugin API
• Inference Engine Transformation API