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
DLDT Intel(R) Deep Learning Deployment Toolkit
DLL Dynamic Link Library
DNN Deep Neural Networks
ELU Exponential Linear rectification Unit
FCN Fully Convolutional Network
FP Floating Point
FPGA Field-Programmable Gate Array
GCC GNU Compiler Collection
GPU Graphics Processing Unit
HD High Definition
IE Inference Engine
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(R) MKL-DNN Intel(R) Math Kernel Library Deep Neural Networks
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 the Inference Engine

Term Description
Batch Number of images to analyze during one call of infer. Maximum batch size is a property of the network and it is set before loading of the network to the plugin. In NHWC, NCHW and NCDHW image data layout representation, the N refers to the number of images in the batch
Blob Memory container used for storing inputs, outputs of the network, weights and biases of the layers
Device (Affinitity) A preferred Intel(R) hardware device to run the inference (CPU, GPU, FPGA, etc.)
Extensibility mechanism, Custom layers The mechanism that provides you with capabilities to extend the Inference Engine and Model Optimizer so that they can work with topologies containing layers that are not yet supported
ICNNNetwork An Interface of the Convolutional Neural Network that Inference Engine reads from IR. Consists of topology, weights and biases
IExecutableNetwork An instance of the loaded network which allows the Inference Engine to request (several) infer requests and perform inference synchronously or asynchronously
IHeteroInferencePlugin Interface that is implemented by the heterogeneity plugin to allow the Inference Engine to set the default affinities for layers by devices before loading the network to the heterogeneous plugin. You can modify affinities manually before loading to the plugin.
IInferencePlugin Interface provided by each plugin to allow the Inference Engine to load ICNNNetwork to the plugin, create Executable network and set special dedicated options for the plugin
IInferRequest Interface that represents the end point of inference on the model loaded to the plugin and represented by executable network. Inputs are set here, outputs should be requested from this interface as well
InferenceEngineProfileInfo Represents basic inference profiling information per layer
Inference Engine A C++ library with a set of classes that you can use in your application to infer input data (images) and get the result
Inference Engine API The basic default API for all supported devices, which allows you to load a model from Intermediate Representation, set input and output formats and execute the model on various devices
Inference Engine Plugin Inference Engine plugin is a software component that contains complete implementation for inference on a certain Intel(R) hardware device: CPU, GPU, VPU, FPGA, etc. Each plugin implements the unified API and provides additional hardware-specific APIs.
Layer catalog or Operations specification A list of supported layers or operations and its parameters. Sets of supported layers are different for different plugins, please check the documentation on plugins to verify if the Inference Engine supports certain layer on the dedicated hardware
Layout 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
OutputsDataMap Structure which contains information about output precisions and layouts
Precision Represents data precision. For example, FP32 is 32-bit floating point, FP16 is 16-bit floating point. Precision can be changed before loading the network to the plugin
PreProcessInfo Class that represents input data for the network. It contains information about input precision, its layout, and pre-processing
ResponseDesc Represents debug information for an error

See Also