HDDL Device¶

Introducing the HDDL Plugin¶

The OpenVINO Runtime HDDL plugin was developed for inference with neural networks on Intel® Vision Accelerator Design with Intel® Movidius™ VPUs. It is designed for use cases that require large throughput for deep learning inference, up to dozens of times more than the MYRIAD Plugin.

Configuring the HDDL Plugin¶

To configure your Intel® Vision Accelerator Design With Intel® Movidius™ on supported operating systems, refer to the Additional Configurations for VPU in Installation Guide.

Note

The HDDL and Myriad plugins may cause conflicts when used at the same time. To ensure proper operation in such a case, the number of booted devices needs to be limited in the ‘hddl_autoboot.config’ file. Otherwise, the HDDL plugin will boot all available Intel® Movidius™ Myriad™ X devices.

Supported networks¶

To see the list of supported networks for the HDDL plugin, refer to the list on the MYRIAD Plugin page.

Supported Configuration Parameters¶

For information on VPU common configuration parameters, see the VPU Plugins. When specifying key values as raw strings (when using the Python API), omit the KEY_ prefix.

In addition to common parameters for both VPU plugins, the HDDL plugin accepts the following options:

Parameter Name	Parameter Values	Default	Description
`KEY_PERF_COUNT`	`YES` / `NO`	`NO`	Enables performance counter option.
`KEY_VPU_HDDL_GRAPH_TAG`	string	empty string	Allows executing network on specified count of devices.
`KEY_VPU_HDDL_STREAM_ID`	string	empty string	Allows executing inference on a specified device.
`KEY_VPU_HDDL_DEVICE_TAG`	string	empty string	Allows allocating/deallocating networks on specified devices.
`KEY_VPU_HDDL_BIND_DEVICE`	`YES` / `NO`	`NO`	Enables the network to be bound to a device. Refer to the ‘vpu_plugin_config.hpp’ file.
`KEY_VPU_HDDL_RUNTIME_PRIORITY`	signed int	0	Specifies the runtime priority of a device among all devices running the same network. Refer to the `vpu_plugin_config.hpp` file.