Digital displays and signs are all around you. You may have seen them cropping up at shopping centers and doctors’ offices. From video walls, to AR fitting mirrors, to ordering menus, digital signs are pervasive and are becoming a part of everyday shopping experience.
This paper introduces Intel software tools recently made available to accelerate deep learning inference in edge devices (such as smart cameras, robotics, autonomous vehicles, etc.) incorporating Intel® Processor Graphics solutions across the spectrum of Intel SOCs.
In this article, we’ll explore the strides Adobe engineers have made over the last few years to enhance Photoshop using OpenGL* and OpenCL™ to increase hardware utilization.
Digital displays and signs are all around you. You may have seen them cropping up at shopping centers and doctors’ offices. From video walls, to AR fitting mirrors, to ordering menus, digital signs are pervasive and are becoming a part of everyday shopping experience.
This paper introduces Intel software tools recently made available to accelerate deep learning inference in edge devices (such as smart cameras, robotics, autonomous vehicles, etc.) incorporating Intel® Processor Graphics solutions across the spectrum of Intel SOCs.
In this article, we’ll explore the strides Adobe engineers have made over the last few years to enhance Photoshop using OpenGL* and OpenCL™ to increase hardware utilization.
Digital displays and signs are all around you. You may have seen them cropping up at shopping centers and doctors’ offices. From video walls, to AR fitting mirrors, to ordering menus, digital signs are pervasive and are becoming a part of everyday shopping experience.
This paper introduces Intel software tools recently made available to accelerate deep learning inference in edge devices (such as smart cameras, robotics, autonomous vehicles, etc.) incorporating Intel® Processor Graphics solutions across the spectrum of Intel SOCs.
In this article, we’ll explore the strides Adobe engineers have made over the last few years to enhance Photoshop using OpenGL* and OpenCL™ to increase hardware utilization.
In this article, we’ll be exploring how to integrate the oneAPI Deep Neural Network (oneDNN) library and the SYCL-based Data Parallel C++ (DPC++) programming language into existing codebases.
This article builds upon the earlier High Performance Queries: GPU vs. PLINQ vs. LINQ and ports this to also support OpenCL devices and adds benchmarking so you can easily compare performance.
Dense Motion Estimation based on Polynomial expansion IntroductionIn this article we will look at dense motion estimation based on polymonial repsentation of image.The polynomial basis representation of the image is obtained by approximating the local neighborhood of image us
This article will go over setting up an Intel Distribution of OpenVINO toolkit module in Azure and explore the considerations for running multiple modules on CPU and GPU on the IEI Tank AIoT Developer Kit.
This paper presents four guidelines that can help guide software developers as they design applications that encourage touch interaction and deliver a memorable user experience on Intel® processor-based pAIOs.
In the conclusion of this two-part series, I detail the best 3D game engine and middleware solutions for Android* tablets, including free, open source, and proprietary options. I also note which have native support for x86 Intel® processors.
This section describes implementation of FaaS inference samples (based on Python 2.7) using Amazon Web Services (AWS) Greengrass and AWS Lambda software.
The intention of this guide is to provide quick steps to create, build, debug, and analyze OpenCL™ applications with the OpenCL™ Code Builder, a part of Intel® Integrated Native Development Environment (Intel® INDE)
It has never been easier for C# desktop developers to write code that takes advantage of the amazing computing performance of modern graphics cards. In this post I will share some techniques for solving a simple (but still interesting) image analysis problem. Source Code https://www.assembla.com/co
This tutorial shows how to use two powerful features of OpenCL™ 2.0: enqueue_kernel functions that allow you to enqueue kernels from the device and work_group_scan_exclusive_add and work_group_scan_inclusive_add
This article is a step-by-step guide on the methodology of dispatching a workload to all OpenCL devices in the platform with the same kernel to jointly achieve a computing task.
This tutorial will guide you through Intel® INDE 2015 installation and demonstrate how to develop native Android* applications that target either x86 based or ARM based processors.
This tutorial will guide you through writing a native “Hello World” Android* app in Visual Studio* through the IDE Integration feature of Intel® INDE 2015.
In this article I will thoroughly discuss about the several aspects of using the revolutionary new Intel® oneAPI HPC Toolkit to deliver a modern code that implements a parallel “stable” sort
The Intel® Computer Vision SDK is a new software development package for development and optimization of computer vision and image processing pipelines for Intel System-on-Chips (SoCs).
Intel® System Studio 2017 Beta has been released. This is the Beta program page which guides you further on Intel® System Studio 2017 Beta new features and enhanced usability experience.
The Face Access Control application is one of a series of IoT reference implementations aimed at instructing users on how to develop a working solution for a particular problem.
Intel is uniquely positioned for AI development—the Intel’s AI Ecosystem offers solutions for all aspects of AI by providing a unified front end for a variety of backend technologies, from hardware to edge devices.
This tutorial will walk you through the basics of using the Deep Learning Deployment Toolkit's Inference Engine (included in the Intel® Computer Vision SDK).
The Intel SDK for OpenCL Applications provides a rich mix of OpenCL extensions and optional features that are designed for developers who want to utilize all resources available on Intel CPUs. This article focuses on device fission, available as a feature in this SDK.
The Intel® Computer Vision SDK is an Intel-optimized and accelerated computer vision software development kit based on the OpenVX standard. The SDK integrates pre-built OpenCV with deep learning support using an included Deep Learning (DL) Deployment toolkit.
Curious about GPGPU programming? Read Rob Farber’s Massively Parallel Programming series. Learn how to get more from your CPU, GPU, APU, DSP, and more.
In his second tutorial, GPGPU expert Rob Farber discusses OpenCL™ memory spaces and the OpenCL memory hierarchy, and how to start thinking in terms of work items and work groups. This tutorial also provides a general example to facilitate experimentation with a variety of OpenCL kernels.
In his third tutorial, GPGPU expert Rob Farber will introduce the OpenCL™ execution model and discuss how to coordinate computations among the work items in a work group
Read Rob Farber’s Massively Parallel Programming series. This fourth article in a series on portable multithreaded programming using OpenCL™ will discuss the OpenCL™ runtime and demonstrate how to perform concurrent computations among the work queues of heterogeneous devices.
This fifth article in a series on portable multithreaded programming using OpenCL™ Rob Farber discusses OpenCL™ buffers and demonstrates how to tie computation to data in a multi-device, multi-GPU environment.
This sixth article in a series on portable multithreaded programming using OpenCL™ where Rob Farber discusses how to calculate data in OpenCL™ and render it with OpenGL within the same application.
This article will demonstrate how to create C/C++ plugins that can be dynamically loaded at runtime to add massively parallel OpenCL capabilities to an already running application
This article will demonstrate how to incorporate OpenCL into heterogeneous workflows via a general-purpose “click together tools” framework that can stream arbitrary messages within a single workstation, across a network of machines, or within a cloud computing framework.
In this article we are going to demonstrate how to optimize Single precision floating General Matrix Multiply (SGEMM) kernels for the best performance on Intel® Core™ Processors with Intel® Processor Graphics.
This tutorial demonstrates how to share surfaces between OpenCL™ and DirectX 11 with Intel ® Processor Graphics on Microsoft Windows, using the surface sharing extension in OpenCL.
This article shows how ordinary differential equations can be solved with OpenCL. In detail it shows how odeint - a C++ library for ordinary differential equations - can be adapted to work with VexCL - a library for OpenCL. The resulting performance is studied on two examples.
Intel just released Intel® System Studio 2018, an all-in-one, cross-platform, comprehensive tool suite for system and IoT device application development.
This article, aimed at developers, will provide a glimpse into this 64-bit, multi-core SOC processor, and gives an overview of the available Intel® technologies, including Intel® HD Graphics 5300.
In this guide, we will show a variety of tools to use as well as features in the Unity software that can help you enhance the performance of your Unity project.
The 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.
In this article, we will introduce the components of INDE and show how developers can use them to create new applications and optimize existing applications. To start with Intel® INDE provides support for IDE integration.
