Discover how AI powered by Intel® Distribution of OpenVINO™ toolkit solves real-world problems
Automated defect detection optimized with the Intel® Distribution of OpenVINO™ toolkit and tested on Intel® DevCloud for the Edge.
Automated inspection, optimized with the Intel® Distribution of OpenVINO™ toolkit, leads to faster, more accurate results.
AI catalyzes operational improvements for manufacturing and smart cities.
Advancing industrial optimization and automation via the integration of machine vision and AI.
SenseNet is a sensorimotor and touch simulator for teaching AI devices how to interact with their environments.
Pipe Sleuth* with optimized inference on Intel® processors.
With technologies such as robotics, computer vision, AI and deep learning (DL), automated logistics operations in hospitals can improve efficiency and streamline human workload.
The Deep Learning Deployment Toolkit from Intel helps deliver optimized inferencing on Intel® architecture, helping bring the power of AI to clinical diagnostic scanning and other healthcare workflows.
Philips demonstrated breakthrough performance for AI inferencing of healthcare workloads run on servers powered by Intel® Xeon® Scalable processors and optimized with the Intel® Distribution of OpenVINO™ toolkit.
Intel® Vision Products and QNAP/IEI are advancing medical breakthroughs with faster results for better patient care.
MaxQ AI Automates and Accelerates Suspected Intracranial Hemorrhage (ICH) Detection with AI-Driven Imaging Insight
Optimized for Intel® architecture, MaxQ AI uses Accipio lx* technology to deliver 3.7x faster performance.
Siemens Healthineers accelerates AI for cardiology, powered by 2nd Generation Intel® Xeon® Scalable processors with Intel® Deep Learning Boost.
Smart inventory tracking optimized by the Intel® Distribution of OpenVINO™ toolkit.
Real-time customer insights with brand recognition powered by the Intel® Distribution of OpenVINO™ toolkit.
Real-time shopper traffic mapping helps retailers compete in an ever-changing marketplace.
The Mindtree Smart Digital Vision Analytics System* enhances security and reduces shrinkage with Intel® technologies.
A comprehensive smart video solution designed for complex urban environments.
Intel’s portfolio of vision products boosts the performance of Agent Video Intelligence Ltd.’s (Agent Vi) cutting-edge computer vision-based surveillance solutions.
The Axxon Intellect PSIM* platform, powered by Intel® Vision Products, was the basis for an integrated video surveillance system designed to meet the monitoring needs across diverse World Cup sites.
GeoVision turbo-charges its deep learning facial recognition solution using Intel® System Studio and OpenVINO™ toolkit.
Gorilla Technology optimizes the efficiency of its Intelligent Video Analytics Recorder (IVAR*) using Intel® Distribution of OpenVINO™ toolkit and Intel® System Studio.
Intel’s robust portfolio of vision products helps Honeywell accelerate video processing time and increase the number of cameras streaming in real-time through a single device.
Targeted analytics for a wide range of industries help increase security, operational efficiency, and insight.
AI catalyzes operational improvements for manufacturing and smart cities.
CyberLink optimizes its facial recognition engine with the Intel® Distribution of OpenVINO™ toolkit.
Intel unveiled the first-generation Intel® Movidius™ Neural Compute Stick (NCS) in July 2017, fueling a community of tens of thousands of developers to create AI solutions to create for some of the world’s most challenging problems.
Intel® AI collaboration with ignio*, a cognitive automation software, allows enterprises to drive agile and proactive IT operations and infrastructure.
An integrated solution advances AI with H2O.ai's industry-leading software, validated and benchmarked on optimized Intel® technologies.
This white paper demonstrates how XSplit VCam* provides streaming gamers and gaming content creators with a reliable solution for intelligent, real-time video background removal.
Verbio uses network edge servers based on Intel® Xeon® Scalable processors with optimized software to maximize performance and take advantage of reduced latency.
Intel® Distribution of OpenVINO™ toolkit optimization delivers high-quality image upscaling at higher speeds with Intel® Core™ processors.
Explainable AI maintains high performance while showing how neural networks build models using the Intel® Distribution of OpenVINO™ tookit.
Working together, Adobe and Intel added the capabilities of the Intel® Distribution of OpenVINO™ toolkit to the Adobe Sensei* on-device SDK, maximizing performance and streamlining workflows.
Asquared IoT Pvt. Ltd. demonstrates a real-time factory solution that analyzes industrial sounds to monitor the quality of operations, such as welding, at a facility using embedded and edge-computing devices. (3:02)
AI-based video solution improves compliance, reporting, and operational efficiencies while helping reduce liability, operational down times, and the risk of worker accidents. (1:00)
Intelligent camera system uses AI and triangulation algorithms at the network edge to analyze and redirect cameras for actionable information and intelligence. (5:57)
This city surveillance solution supports up to four simultaneous video channels of facial recognition, tracking, and gender recognition. (1:06)
Intelligent real-time video analytics platform performs facial recognition, site detection, license plate recognition, and loitering detection analytics on multiple video channel streams. (4:46)
With this computer vision solution, users create computer vision detectors to recognize people, events, and objects. (3:25)
This AI retail solution uses analytics at the edge to customize and transform the customer journey while helping drive success in today's competitive marketplace. (3:54)
By pairing deep learning models with Intel® Distribution for OpenVINO™ toolkit for AI at the edge, Xnor.ai demonstrates how large-scale, fully automated video analytics solutions can be deployed affordably. (1:56)
Explore how developers can use this toolkit to create high-performance applications, incorporate industry-standard frameworks and training models, and more. (13:46)
Take a closer look at access to traditional computer vision components, deep learning tools, Intel® architecture-based platforms support, and operating system support. (14:35)
Learn how Intel products, developer tools, programs, and ecosystem solutions accelerate the development and deployment of edge computing solutions. This eBooklet contains 1) hardware based on Intel® unmatched silicon portfolio, 2) Intel® deployment software and tools to accelerate for edge AI inference, 3) developer ready hardware including Foundation Kits and Vision Accelerator Design Products, 4) Intel® Scalable Solutions and 5) Intel® Partner Enablement Programs to support the scale of our Edge Computing ecosystem community.
Featured solutions include the Intel® IoT Market Ready Solution powered by Intel® Distribution of OpenVINO™ toolkit, Intel® IoT RFP Ready Kit powered by Intel® Distribution of OpenVINO™ toolkit, solution briefs, and case studies from partners with solutions based on Intel® Architecture and enabled with Intel Distribution of OpenVINO toolkit. This booklet is intended to promote a wide selection of Intel®-based commercial grade, ready-to-scale offerings that are optimized by Intel Distribution of OpenVINO toolkit from Edge AI ecosystem partners.
Considerations for building safer, smarter cities.
Today, greater than half of the world’s population reside in urban areas, including densely populated cities. This boom in urbanization is changing the ways that we live, conduct business, access education and require public services. The sheer rate of unplanned migration can be a strain on aging infrastructure and the people who work to keep citizens safe in cities.
Helping Our Community Succeed
“Our strategic focus is bringing artiﬁcial intelligence on the edge but it has always been challenging to implement eﬃcient algorithm balancing [for] the workload between the diﬀerent hardware components. OpenVINO™ toolkit bridges this gap and gives us a unique tool that speeds up the path to production of our and our partners' solutions.”
— Fabrizio Del Maﬀeo, Vice President and Managing Director, AAEON Technology
Defect inspection for contact lenses is difficult and inefficient since defects of contact lens are usually non-rule based. Additionally, transparency of the contact lens increases the inspection difficulty, accuracy, and overall manufacturing throughput. ADLINK AI contact lens inspection solution uses well-validated EOS-i6000-M Series vision system featured 9th Gen Intel® Core™ i7-9700E processor and 4x Intel® Movidius™ Myriad™ X VPUs executes onsite contact lens inspection via AI software partner-LEDA technology’s easy-to-use AI contact lens inspection software. Adlink choses Intel because it they are affordable, cost-effective, and well-validated. With this Intel based solution, optimized with the Intel® Distribution of OpenVINO™ toolkit, contact lens manufacturers get a reliable all-defect-inspection solution for their products. Meanwhile, the manufacturing throughput has been shown to improve by up to 50x with greater than 95% accuracy when compared to manual inspection.
— Tim Juan, Senior Director, ADLINK Smart Factory Business Center
“With OpenVINO™ toolkit the results have been impressive, enabling us to move from supporting two cameras to 20 with one developer in under three weeks. We will be able to fully scale our solutions to the edge with the right performance per dollar while leveraging Intel® Movidius™ Vision Processing Unit (VPU) and Intel® FPGA solutions.”
— Zvika Ashani, CTO and Co-founder, Agent Vi
Amazon Web Services (AWS)
“Intel’s wide range of deep learning acceleration silicon allows us to oﬀer AWS Greengrass* to customers who require powerful yet cost-eﬀective AI solutions at the edge. With [the] OpenVINO™ toolkit integrated [with] Greengrass devices, these customers are enabled to build cloud machine learning models that they can deploy at the edge to perform deep learning tasks in real time, which can be tailored to their speciﬁc performance needs. Our partnership with Intel in this space is essential to us providing customers a wide variety of cost-eﬀective machine learning solutions.”
— Satyen Yadav, General Manager, IoT Ecosystem, AWS
“Because we serve customers with so many different needs, it’s important to quickly achieve the right balance of price and performance for each of our applications. Intel® DevCloud for the Edge lets us test multiple platforms in parallel. That’s a lot of time savings—and time is money—so it’s a no-brainer.”
— Eduard Vazquez, Research Technical Manager, AnyVision
“Now through the Intel® AI: In Production program, CONEX will be able to rapidly go from our validated prototypes to actual end products. Thanks to this new path to production, we will be deploying an AI-enhanced, point-of-sale retail device to some of the largest cosmetic goods retailers as soon as this spring.”
— Nicolas Lorin, President, CONEX
"CyberLink is a general member of Intel® IoT Solutions Alliance, which aims to deliver the best AI at the edge solutions. We have optimized our key software applications with Intel® Distribution of OpenVino™ toolkit. We have integrated OpenVINO toolkit into a number of our creative software’s AI features such as styling videos into live paintings for video editing in PowerDirector* and photo deblurring in PhotoDirector*.
FaceMe*, our edge-based AI facial recognition SDK solution, is highly optimized for the OpenVINO toolkit. With OpenVINO toolkit acceleration enabled, the speed of FaceMe's facial recognition is improved by 500 percent (from 4.5 fps to 23 fps). Compared to running on Celeron® processor, FaceMe runs 17.4x faster on Intel® Movidius™ Myriad™ X VPU and saves 72 percent CPU usage.
Using these Intel® technologies with FaceMe not only enables use cases otherwise impractical or even impossible, but it can generate significant savings for our customers."
— Richard Carriere, Senior VP of Global Marketing and GM of Americas
“We use machine learning to probe and understand a neural network on a very fundamental level, and then build up a very sophisticated mathematical understanding of the network during that process. We then use AI a second time to generate an entirely new family of neural networks that is considerably more compact than the original, as good as the original from a functional standpoint, but works a lot faster. That is the ‘generative’ in Generative Synthesis*. What’s more, the process 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 the Intel Distribution of OpenVINO toolkit, we saw up to 6.18x improvement in FP32 data types and 10.51x in INT8 running on 2nd Gen Intel® Xeon® Scalable processor-based platforms.”
— Sheldon Fernandez, CEO, DarwinAI
“Based on the Intel® Distribution of OpenVINO™ toolkit, we have developed a highly accurate, highly stable intelligent defect detection system for the tire industry. OpenVINO toolkit not only helps with significant inspection system performance improvement but also helps us achieve millisecond-level defect detection performance on a normal industrial computer with more than 99.9 percent accuracy. The solution helps the customer to cut labor costs by approximately USD 49,000 per production line per year, and the production efficiency has been improved greatly.”
— Michael Li, Founder and CEO, DeepSight
"OpenVINO™ toolkit enables us to quickly develop a new smart camera solution under the SMARTernak* platform that implements visual intelligence at the edge. We’re able to finish an initial working product from two months to under two weeks by leveraging ready-to-use ML models, easy-to-use tooling, and Inference Engine API, while still achieving up to 10x inference throughput, even when it’s run on a single board computer at the edge that’s accelerated by Intel® Movidius™ Neural Compute Stick. The OpenVINO toolkit-powered smart camera offers 50 percent of the cost for our customers if compared to our wearable-based solution. For certain functionality (e.g., herd counting), the cost is reduced even further, as one camera is able to count the whole herd. OpenVINO toolkit certainly has tremendous value proposition for certain use cases that will greatly benefit our customers."
— Andri Yadi, CEO, DycodeX
European Space Agency (ESA)
"The market for remote sensing space-based application is fundamentally limited by the up and downlink bandwidth and the onboard compute capability on satellites. The work done by ESA shows how the compute capability on these platforms can be vastly increased by leveraging emerging COTS system-on-chip technologies. The orders of magnitude increase in processing power can then be applied to consuming data at the source rather than on the ground. This allows the deployment of value-added applications in space, which consume a tiny fraction of the downlink bandwidth that would be otherwise required. The solution, based on Intel® Movidius™ VPU, has the potential to revolutionize Earth Observation (EO) and other remote sensing applications, reducing the time and cost to deploy newly added value services to space by a great extent, at least 30 percent, compared with the current state. "
— Gianluca Furano, Data Systems Engineer
— Massimiliano (Max) Pastena, Earth Observation Technology Management Engineer
“With the OpenVINO™ toolkit running on existing Intel® processors, GE Healthcare has achieved a 3.3x improvement in deep learning optimization, which enables early prioritization and escalation of critical conditions to ensure faster treatment for our patients. Intel® technologies will enable GE Healthcare to extend AI solutions across multiple imaging modalities to transform radiologist workflows and patient care.”
— Karley Yoder, VP and GM, Edison AI, GE Healthcare
"The implementation of OpenVINO™ toolkit has not only enhanced our processing capacity signiﬁcantly as edge AI developers, but also surpassed for an optimal, ideal platform capable of fulﬁlling a wide range of AI applications. Statistically, it uplifted us to realize a performance gain of eight to tenfold after employing OpenVINO toolkit, making simultaneous, real-time video processing possible at greater capacities. Accompanied with detailed, intuitive documentations on model optimizer and inference engine API, the use of OpenVINO toolkit has also strengthened our development cycle of deep learning driven features.”
— Thomas Lee, Sales Engineer, GeoVision Inc.
“At the Fengyuan Train Station, with the deployment of our OpenVINO™ toolkit-enabled system, we saw a 50 percent reduction in our emergency response time and a 70 percent decrease in Yellow Line crossings.”
—Taiwan Railway Bureau, MOTC
“We improved our Traffic Violation Management using an OpenVINO toolkit-optimized solution at one of our busiest intersections. Once online, we recorded a 50 percent drop in illegal left turn violations.”
— Kaohsiung Transportation Bureau
Healthcare staff are busy and overworked, but the movement of supplies never stops. A single robot can do the work of up to three people and hold over 10x more weight than any single person, making hospital logistics a good job for robots. To meet the sensory requirements of the hospital logistics distribution robots, Gwell Medical (formerly known as "MROBOT") Hospital Logistics Robot is powered by 7th generation Intel® Core i5/i7 processors (Intel® Core™ i5-7500 and i7-7700 processors), Intel® Movidius™ Myriad 2 VPUs, and optimized with Intel® Distribution of OpenVINO™ toolkit (allowing for a 5.8x increase in FPS throughput). The integration of these products and technologies provides a more energy-efficient and more rationally designed solution with unique, optimized processing capabilities for deep visual loads, which can meet the requirements of hospital logistics distribution for low-latency perception capabilities. These Intel based Hospital Logistic Robots have been applied successfully in more than 40 hospitals nationwide, including several Wuhan hospitals.”
— Harrison LV, CTO, Gwell Medical (formerly known as "MROBOT")
“Because developers can quickly evaluate the performance of their applications in multiple edge computing systems by using Intel® DevCloud for the Edge, they can not only shorten the inspection time to go to market, they can also expect tremendous benefits in terms of investment and maintenance in verification equipment. We are confident that Intel DevCloud for the Edge will accelerate and streamline operations and create new value for more IoT businesses and for more customers.”
— Tomohiro Nagao, Senior Manager, Hitachi Ltd., Healthcare Business Unit
"By using the OpenVINO™ toolkit solution, we realize up to 90 percent faster speeds in the loading AI model, and that leads to undiminished performance on the CPU in the Intel® NUC system without GPU acceleration. As a result, there is a cost reduction of more than 50 percent, and the size is reduced by more than 70 percent. These improvements enhance a new value of mobility of medical AI solution, because the end-users can now easily implement AI modules into their Hospital Information System and maintain the system using Intel NUC with OpenVINO toolkit technology. Patients can be provided with a faster and more accurate diagnosis, which may lead to a better prognosis. Also, patients can be provided with the healthcare reports from the AI solution to easily know their health information."
— Dongmin Kim, Ph.D., CEO, JLK Inspection
“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
“The framework independent OpenVINO™ toolkit inference engine allowed NCTech to build an advanced data privacy engine with similar accuracy as seen in Google Street View*. Data privacy is to remove identifiable personal information (such as people's faces or car number plates) from street-level images. We use AI to detect people's faces and car number plates in images, and then we blur them so they are not identifiable. To achieve the best accuracy, we utilize different AI frameworks for training and OpenVino toolkit as our inference engine.
This improvement in speed allows us to process and stitch data sets captured per one mile in 30 minutes instead of the two hours it took for the original system, reducing costs by up to 75 percent."
— Cameron Ure, CEO and Co-founder, NCTech Ltd.
"We at QNAP, along with one of the leading hospitals in Taiwan, have jointly worked to bring in an innovative assistive healthcare solution in the area of skin disease diagnosis. Based on the strong technical foundation of Intel® Distribution of OpenVINO™ toolkit, this is a non-invasive, AI-based solution that helps to identify five different types of skin diseases that were difficult to diagnose earlier. Intel Distribution of OpenVINO toolkit has played an instrumental role in achieving the expected performance and accuracy of the solution."
— Y.T. Lee, Vice President, QNAP
“QuEST used Intel® Distribution of OpenVINO™ toolkit for our solution to helping radiologists identify lung nodules through Computer Aided Detection (CAD) models. We obtained impressive results while we employed model inference on Intel® Edge devices without compromise in performance. We achieved 33x performance improvement in running the OpenVINO toolkit model on Intel® Core™ processor-based edge devices when compared to the unoptimized baseline model. Intel® architecture with OpenVINO toolkit offers an affordable solution when trying to balance accuracy and complexity of the model and real time performance. This means we don’t have to opt for new, expensive, hardware-based deployment around healthcare institutions, resulting in a strong TCO advantage."
— Dr. Rubayat Mahmud, Director of Sales and Business Development, QuEST Global
Rosmart Technology Co., Ltd.
"Defect detection is the key process in electronic component manufacturing. However, manual inspection performed by operators cannot meet the quality and efficiency requirements, otherwise labor cost will be increased quickly. We design and implement automatic defects detection machine based on Deep Learning algorithm that is accelerated by Intel® Distribution of OpenVINO™ toolkit. We have seen great improvement of the inspection accuracy from 87% to 98%, and cuts labor cost by nearly RMB 500,000 (an equivalent of 70,500 US dollar) per production line per year. In addition, Intel® Distribution of OpenVINO™ toolkit not only helps us to deploy AI model on Windows10 platform easily, but also to cut computing cost by nearly RMB 10,000 (an equivalent of 1400 US dollar) per equipment comparing to the original GPU inference solution."
— Johnson Li, Co-Founder and VP of Guangdong Rosmart Technology Co., Ltd.
“Topaz Labs worked with Intel on our latest release of our Gigapixel AI* for Photo. Gigapixel AI uses AI/ML and deep-learning methods to enlarge images and add detail. With OpenVINO™ toolkit implementation, we saw approximately an 8.73x performance increase of photo upscaling up to 400 percent while preserving image quality using AI/ML. The model optimizations through OpenVINO toolkit and targeting Gen 11 graphics resulted in a 2.93x performance benefit on Ice Lake compared to the competition and a 1.74x Gen over Gen!”
— Eric Yang, CEO, Topaz Labs
“Sharpen AI* is the first sharpening and shake reduction software that can tell the difference between real detail and noise. Our customers have seen a greater than 7x improvement in sharpening, just under an 11x increase when stabilizing, and a 10.5x improvement when focusing since the implementation of OpenVINO toolkit into our Sharpen AI application.”
— Russell Tarpley, VP of Products, Topaz Labs
“DeNoise AI* uses artificial intelligence and machine learning, which together allow the elimination of noise and the recovery of crisp detail in photos images with the first AI powered noise reduction tool. Gen 11 GPU performance on the 10th Generation Intel® Core™ processor is 1.86x better than KBL-R and 3.57x better than Ryzen 3700U* for denoising images. Optimization of DeNoise AI using OpenVINO toolkit produced over a 7x performance improvement compared to the previous version!”
— Albert Yang, CTO, Topaz Labs
Touch Cloud, Inc.
"OpenVINO™ toolkit improves inference performance up to 10 times faster, reduces CPU loading, and enables an edge device to support more channels."
— Simon Lee, Co-founder and CEO, Touch Cloud, Inc.
Intel® Distribution of OpenVINO™ toolkit and DL streamer helped us to achieve greater performance using the same range of processors with significant reduction of development time. By leveraging the decoding and deep learning computing capability of Intel’s built-in GPU on the processors, our deep learning engines are now distributed at various layers of our fog computing framework in video-based IoT solutions. Intel® Distribution of OpenVINO™ toolkit and DL streamer helped us to quickly come up with a new generation of applications in intelligent traffic management and intelligent video analytics, and the smart urban analytics domain with a very low cost of ownership to our customers, thus enabling them to deploy these solutions at a much larger scale.
— Tuhin Bose, VP and CTO, Videonetics Technology Pvt. Limited
Vispera Information Technologies
As a member of the Intel® AI: In Production Program, we use Intel® DevCloud for the Edge and Intel® Distribution of OpenVINO™ toolkit extensively, where CPU-run engines that are optimized with the latter are currently in production. So far, We have used DevCloud for the Edge primarily for benchmarking purposes, which enabled us to determine the optimal edge hardware configurations, algorithmic choices and engine parameters, production-level load estimations and scalability assessment under different production scenarios. With our solution, the results obtained from the exercise in DevCloud for the Edge show that computing deep neural networks on CPU has become on par with GPU in terms of performance, and it is constantly improving with newer tools like int8 quantization, making advanced edge inference solutions like ours feasible with CPU. Using OpenVINO toolkit, the per image inference time speeds up by 10x as compared to unoptimized CPU execution, which allows us to run our solution as a real-time IoT application operating on Intel® Xeon™ processors at multiple stores.
— Erdem Yoruk, Chief Scientist, Vispera Information Technologies
"The implementation of OpenVINO™ toolkit in the VSBLTY product suite for true edge-based computer vision in retail environments was a god-send at a time when we really needed it. We have customer implementations all over the world. We built the fault tolerance for low bandwidth and intermittent internet connectivity that nicely handles the audience measurement analytics we are doing. OpenVINO toolkit provided the software plumbing we so desperately needed to run our computer vision algorithms on the edge in real time without the need for any bandwidth, let alone network connectivity. VSBLTY gained huge performance increases, in many cases 4 to 5x as fast, in the processing of our computer vision machine learning models.”
— Tim Huckaby, CTO, VSBLTY
"Due to our modular AI-Training Platform, we are using many different AI frameworks, where ONNX* simplifies the deployment processes of trained models, since they adhere to one shared standard. OpenVINO™ toolkit is backed by a strong open-source community, great documentation, a deep integration into OpenCV, and an easier to use API compared to competitors like TensorRT*, CuDNN*, etc. It also makes it possible to switch between different hardware for execution, like CPU, GPU, FPGAs, and Intel® Movidius™ technology, on-the-fly."
— Sebastian Borchers, Co-founder, Wahtari
"As an Intel® AI: In Production Program member, WonderStore is dedicated to Intel® Distribution of OpenVINO™ toolkit, which accelerates CV models on Intel-based hardware. Because WonderStore creates specific CV models for specific customers, we need OpenVINO to be able to run many models smoothly, using affordable hardware. By leveraging a huge repository of publicly-available models for the CV developer community offered by the toolkit, WonderStore can provide in-store analytics and detailed shopper profiles: not just gender and age, but also which clothing and accessory brands they wear. "
— Reinier van Kleij, PhD., Partner, Business & Technology, CTO, WonderStore
"WonderStore uses Intel® DevCloud for the Edge to train models with data sets of over 30,000 pictures in half the time vs. on-premise servers. Intel® DevCloud for the Edge allows us to choose our hardware configuration to optimize training, enabling us to create vertical CV models for each customer's needs."
— Reinier van Kleij, CTO, WonderStore
The need for intelligence at the edge has never been greater!
Learn how Intel is speeding up the development of edge intelligence solutions with Intel® Edge Software Hub. This one-stop resource provides partners and developers access to vertical-specific software packages (Industrial, Retail, and Vision) enabled by Edge AI technologies, with complementary development resources. This webinar will cover an overview of the Edge Software Hub and ecosystem case studies, as well as how partners can engage.
The Intel® Distribution of OpenVINO™ toolkit 2020.4 release includes improved performance with support for the Bfloat16 data type for inferencing, using the 3rd generation Intel® Xeon® Scalable processor. This release also allows greater compatibility of models by supporting directly reading Open Neural Network Exchange (ONNX*) model format to the Inference Engine.
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Intel® DevCloud for the Edge is a cloud-based sandbox that enables enterprise developers to test, prototype, and benchmark AI edge models across multiple platforms in real time and from nearly anywhere in the world. Register today to get access to an innovative sandbox for AI at the edge.
At a gathering of industry influencers, Intel welcomed the next wave of AI with updates on new products designed to accelerate AI system development and deployment from cloud to edge. Intel demonstrated its Intel® Nervana™ Neural Network Processors (NNP) for training (NNP-T1000) and inference (NNP-I1000)—Intel’s first purpose-built AI-specific architecture for complex deep learning with incredible scale and efficiency for cloud and data center customers.
Learn More about Intel® AI: In Production
Intel® AI: In Production streamlines the path from prototype to production for AI at the edge solutions in order to deliver real-world results.