Embedded Systems Solutions


Broaden Your Vision with the OpenVINO Toolkit
The Intel® Distribution of OpenVINO toolkit accelerates inference performance for computer vision and deep learning applicationsup to 19x with heterogeneous execution on many Intel® platforms and hardware accelerators (CPU, GPU, Intel® Movidius Neural Compute Stick, and FPGA).

Enables CNN-based deep learning inference on the edge
Supports heterogeneous execution across computer vision acceleratorsCPU, GPU, Intel® Movidius Neural Compute Stick, and FPGAusing a common API
Speeds time to market via a library of functions and preoptimized kernels
Includes optimized calls for OpenCV and OpenVX

Intel� Deep Learning Deployment Toolkit

Increase Deep Learning Workload Performance on Public Models using OpenVINO™ Toolkit & Intel� Architecture

Intel� Optimizations improve scikit-learn efficiency closer to native code speeds on Intel Core processors

Intel� Distribution for Python* 2019 Scikit-learn* Accelerations on Amazon* AWS EC2

Model Optimizer

Model Optimizer is a cross-platform command-line tool that facilitates the transition between the training and deployment environment, performs static model analysis, and adjusts deep learning models for optimal execution on end-point target devices.

Inference Engine

The Inference Engine deployment process assumes you used the Model Optimizer to convert your trained model to an Intermediate Representation. The scheme below illustrates the typical workflow for deploying a trained deep learning model.

The diagram below illustrates the typical workflow for deploying a trained deep learning model:

Workflow for deploying a trained deep learning

Model Optimizer Developer Guide: https://software.intel.com/en-us/articles/OpenVINO-ModelOptimizer

Pre trained Models

The Intel® Distribution of OpenVINO toolkit includes two sets of optimized models that can expedite development and improve image processing pipelines for Intel® processors. Use these models for development and production deployment without the need to search for or to train your own models.