Glossary

Acronyms and Abbreviations

Abbreviation

Description

API

Application Programming Interface

AVX

Advanced Vector Extensions

clDNN

Compute Library for Deep Neural Networks

CLI

Command Line Interface

CNN

Convolutional Neural Network

CPU

Central Processing Unit

CV

Computer Vision

DL

Deep Learning

DLL

Dynamic Link Library

DNN

Deep Neural Networks

ELU

Exponential Linear rectification Unit

FCN

Fully Convolutional Network

FP

Floating Point

GCC

GNU Compiler Collection

GPU

Graphics Processing Unit

HD

High Definition

IR

Intermediate Representation

JIT

Just In Time

JTAG

Joint Test Action Group

LPR

License-Plate Recognition

LRN

Local Response Normalization

mAP

Mean Average Precision

Intel® OneDNN

Intel® OneAPI Deep Neural Network Library

MO

Model Optimizer

MVN

Mean Variance Normalization

NCDHW

Number of images, Channels, Depth, Height, Width

NCHW

Number of images, Channels, Height, Width

NHWC

Number of images, Height, Width, Channels

NMS

Non-Maximum Suppression

NN

Neural Network

NST

Neural Style Transfer

OD

Object Detection

OS

Operating System

PCI

Peripheral Component Interconnect

PReLU

Parametric Rectified Linear Unit

PSROI

Position Sensitive Region Of Interest

RCNN, R-CNN

Region-based Convolutional Neural Network

ReLU

Rectified Linear Unit

ROI

Region Of Interest

SDK

Software Development Kit

SSD

Single Shot multibox Detector

SSE

Streaming SIMD Extensions

USB

Universal Serial Bus

VGG

Visual Geometry Group

VOC

Visual Object Classes

WINAPI

Windows Application Programming Interface

Terms

Glossary of terms used in OpenVINO™

Batch
Number of images to analyze during one call of infer. Maximum batch size is a property of the model set before its compilation. In NHWC, NCHW, and NCDHW image data layout representations, the ‘N’ refers to the number of images in the batch.
Device Affinitity
A preferred hardware device to run inference (CPU, GPU, GNA, etc.).
Extensibility mechanism, Custom layers
The mechanism that provides you with capabilities to extend the OpenVINO™ Runtime and Model Optimizer so that they can work with models containing operations that are not yet supported.
layer / operation
In OpenVINO, both terms are treated synonymously. To avoid confusion, “layer” is being pushed out and “operation” is the currently accepted term.
OpenVINO™ <code>Core</code>
OpenVINO™ Core is a software component that manages inference on certain Intel(R) hardware devices: CPU, GPU, GNA, etc.
OpenVINO™ API
The basic default API for all supported devices, which allows you to load a model from Intermediate Representation or convert from ONNX, PaddlePaddle file formars, set input and output formats and execute the model on various devices.
OpenVINO™ Runtime
A C++ library with a set of classes that you can use in your application to infer input tensors and get the results.
<code>ov::Model</code>
A class of the Model that OpenVINO™ Runtime reads from IR or converts from ONNX, PaddlePaddle formats. Consists of model structure, weights and biases.
<code>ov::CompiledModel</code>
An instance of the compiled model which allows the OpenVINO™ Runtime to request (several) infer requests and perform inference synchronously or asynchronously.
<code>ov::InferRequest</code>
A class that represents the end point of inference on the model compiled by the device and represented by a compiled model. Inputs are set here, outputs should be requested from this interface as well.
<code>ov::ProfilingInfo</code>
Represents basic inference profiling information per operation.
<code>ov::Layout</code>
Image data layout refers to the representation of images batch. Layout shows a sequence of 4D or 5D tensor data in memory. A typical NCHW format represents pixel in horizontal direction, rows by vertical dimension, planes by channel and images into batch. See also [Layout API Overview](@ref openvino_docs_OV_UG_Layout_Overview).
<code>ov::element::Type</code>
Represents data element type. For example, f32 is 32-bit floating point, f16 is 16-bit floating point.
plugin / Inference Device / Inference Mode
OpenVINO makes hardware available for inference based on several core components. They used to be called “plugins” in earlier versions of documentation and you may still find this term in some articles. Because of their role in the software, they are now referred to as Devices and Modes (“virtual” devices). For a detailed description of the concept, refer to [Inference Modes](@ref openvino_docs_Runtime_Inference_Modes_Overview) and [Inference Devices](@ref openvino_docs_OV_UG_Working_with_devices).
Tensor
A memory container used for storing inputs and outputs of the model, as well as weights and biases of the operations.