Working with devices

The OpenVINO Runtime provides capabilities to infer deep learning models on the following device types with corresponding plugins:

Plugin

Device types

CPU

Intel® Xeon®, Intel® Core™ and Intel® Atom® processors with Intel® Streaming SIMD Extensions (Intel® SSE4.2), Intel® Advanced Vector Extensions 2 (Intel® AVX2), Intel® Advanced Vector Extensions 512 (Intel® AVX-512), Intel® Vector Neural Network Instructions (Intel® AVX512-VNNI) and bfloat16 extension for AVX-512 (Intel® AVX-512_BF16 Extension)

GPU

Intel® Graphics, including Intel® HD Graphics, Intel® UHD Graphics, Intel® Iris® Graphics, Intel® Xe Graphics, Intel® Xe MAX Graphics

VPUs

Intel® Neural Compute Stick 2 powered by the Intel® Movidius™ Myriad™ X, Intel® Vision Accelerator Design with Intel® Movidius™ VPUs

GNA

Intel® Speech Enabling Developer Kit ; Amazon Alexa* Premium Far-Field Developer Kit ; Intel® Pentium® Silver Processors N5xxx, J5xxx and Intel® Celeron® Processors N4xxx, J4xxx (formerly codenamed Gemini Lake) : Intel® Pentium® Silver J5005 Processor , Intel® Pentium® Silver N5000 Processor , Intel® Celeron® J4005 Processor , Intel® Celeron® J4105 Processor , Intel® Celeron® J4125 Processor , Intel® Celeron® Processor N4100 , Intel® Celeron® Processor N4000 ; Intel® Pentium® Processors N6xxx, J6xxx, Intel® Celeron® Processors N6xxx, J6xxx and Intel Atom® x6xxxxx (formerly codenamed Elkhart Lake) ; Intel® Core™ Processors (formerly codenamed Cannon Lake) ; 10th Generation Intel® Core™ Processors (formerly codenamed Ice Lake) : Intel® Core™ i7-1065G7 Processor , Intel® Core™ i7-1060G7 Processor , Intel® Core™ i5-1035G4 Processor , Intel® Core™ i5-1035G7 Processor , Intel® Core™ i5-1035G1 Processor , Intel® Core™ i5-1030G7 Processor , Intel® Core™ i5-1030G4 Processor , Intel® Core™ i3-1005G1 Processor , Intel® Core™ i3-1000G1 Processor , Intel® Core™ i3-1000G4 Processor ; 11th Generation Intel® Core™ Processors (formerly codenamed Tiger Lake) ; 12th Generation Intel® Core™ Processors (formerly codenamed Alder Lake)

Arm® CPU

Raspberry Pi™ 4 Model B, Apple® Mac mini with M1 chip, NVIDIA® Jetson Nano™, Android™ devices

OpenVINO runtime also has several execution capabilities which work on top of other devices:

Capability

Description

Multi-Device execution

Multi-Device enables simultaneous inference of the same model on several devices in parallel

Auto-Device selection

Auto-Device selection enables selecting Intel device for inference automatically

Heterogeneous execution

Heterogeneous execution enables automatic inference splitting between several devices (for example if a device doesn’t support certain operation )

Automatic Batching

Auto-Batching plugin enables the batching (on top of the specified device) that is completely transparent to the application

Devices similar to the ones we have used for benchmarking can be accessed using Intel® DevCloud for the Edge, a remote development environment with access to Intel® hardware and the latest versions of the Intel® Distribution of the OpenVINO™ Toolkit. Learn more or Register here.

Features Support Matrix

The table below demonstrates support of key features by OpenVINO device plugins.

Capability

CPU

GPU

GNA

Arm® CPU

Heterogeneous execution

Yes

Yes

No

Yes

Multi-device execution

Yes

Yes

Partial

Yes

Automatic batching

No

Yes

No

No

Multi-stream execution

Yes

Yes

No

Yes

Models caching

Yes

Partial

Yes

No

Dynamic shapes

Yes

Partial

No

No

Import/Export

Yes

No

Yes

No

Preprocessing acceleration

Yes

Yes

No

Partial

Stateful models

Yes

No

Yes

No

Extensibility

Yes

Yes

No

No

For more details on plugin specific feature limitation, see corresponding plugin pages.