Develop applications and solutions that emulate human vision with the Intel® Distribution of OpenVINO™ toolkit. Based on convolutional neural networks (CNN), the toolkit extends workloads across Intel® hardware (including accelerators) and maximizes performance.
Accelerate and deploy neural network models across Intel® platforms with a built-in model optimizer for pretrained models and an inference engine runtime for hardware-specific acceleration.
Develop and optimize classic computer vision applications built with the OpenCV library and other industry tools.
Harness the performance of Intel®-based accelerators: CPUs, iGPUs, FPGAs, VPUs, Intel® Gaussian & Neural Accelerators, and IPUs.
Intel offers a powerful portfolio of scalable hardware and software solutions, powered by the Intel Distribution of OpenVINO toolkit, to meet the various performance, power, and price requirements of any use case. See how the toolkit can boost your inference applications across multiple deep neural networks with high throughput and efficiency.
AI researchers, application developers, data scientists, and implementers who require:
Intel teamed up with Philips to deliver high performance, efficient deep-learning inference on X-rays and computed tomography (CT) scans without the need for accelerators. The solution runs on servers powered by Intel® Xeon® Scalable processors and was optimized by the Intel Distribution of OpenVINO toolkit.
Track and conduct an analysis on classroom metrics, such as attendance, participation, and engagement.
Detect industrial anomalies using computer vision and time-series analysis.
Brain Tumor Image Segmentation
Segment brain tumors in raw MRI images by applying the U-Net architecture.
“Intel® Xeon® Scalable processors and OpenVINO toolkit appears to be the right solution for medical imaging AI workloads. Our customers can use their existing hardware to its maximum potential, without having to complicate their infrastructure, while still aiming to achieve quality output resolution at exceptional speeds."
— Vijayananda J., chief architect and fellow, Data Science and AI, Philips HealthSuite Insights, India
"Our strategic focus is bringing artificial intelligence on the edge. The challenge has been in implementing efficient algorithms to balance the workload between different hardware components. The OpenVINO toolkit bridges this gap, giving us a unique tool that speeds up the path to production, both for us and our partners."
— Fabrizio Del Maffeo, vice president and managing director, AAEON Technology, Europe
"AI presents tremendous opportunity for industrial automation. With machine vision extensively used for guidance, identification, gauging, and inspection in manufacturing processes, the OpenVINO toolkit integrates deep learning tools, computer vision acceleration, and support for heterogeneous execution environments with high inference speed and accuracy for machine vision.”
— Edgar Chen, general manager of the embedded platforms and modules business unit, ADLINK*
"With the OpenVINO toolkit, we are now able to optimize inferencing across silicon from Intel, exceeding our throughput goals by almost six times. We want to not only keep deployment costs down for our customers, but also offer a flexible, high-performance solution for a new era of smarter medical imaging. Our partnership with Intel allows us to bring the power of AI to clinical diagnostic scanning and other healthcare workflows in a cost-effective manner."
— David Chevalier, principal engineer, General Electric (GE) Healthcare*
"What's more, the process [Generative Synthesis] is entirely complementary to the Intel® Distribution of OpenVINO™ toolkit, which does a superior job of optimizing mode performance on Intel® architecture. Specifically, by leveraging [sic] OpenVINO™, we saw up to 6.18 times improvement in FP32 data types and 10.51 times in INT8 running on 2nd Gen Intel® Xeon® Scalable processor-based platforms."
— Sheldon Fernandez, CEO, DarwinAI
"The original FDA-approved product release of Accipio Ix took an average of 4.1 minutes to process a radiology exam. Thanks to the optimizations with Intel Distribution of OpenVINO toolkit, the average processing time for the first 4,000 exams at Capital Health has been 1.4 minutes."
— Steve Kohlmyer, VP Research and Clinical Collaborations, MaxQ AI
Deep Learning for Healthcare Imaging
Intel and GE* bring the power of AI to clinical diagnostic scanning and other healthcare workflows.
Robotics with AI for Industry 4.0
NexCOBOT offers a flexible, modular robotics solution that integrates AI with machine vision using tools from Intel.
The developer community and our tech experts provide the help you need in this public forum.
The OpenVINO toolkit is an open source product. It contains the Intel DLDT for Intel® processors (for CPUs), Intel® Processor Graphics (for GPUs), and heterogeneous support. It includes an open model zoo with pretrained models, samples, and demos.
Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.
Notice revision #20110804