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

API References