{"id":23141,"date":"2019-08-26T08:30:10","date_gmt":"2019-08-26T15:30:10","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=23141"},"modified":"2019-08-27T08:35:23","modified_gmt":"2019-08-27T15:35:23","slug":"develop-multiplatform-computer-vision-solutions-with-intel-distribution-of-openvino-toolkit","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2019\/08\/26\/develop-multiplatform-computer-vision-solutions-with-intel-distribution-of-openvino-toolkit\/","title":{"rendered":"Develop Multiplatform Computer Vision Solutions with Intel\u00ae Distribution of OpenVINO\u2122 Toolkit"},"content":{"rendered":"\n<p style=\"text-align:center\"><em>Sponsored Post<\/em><\/p>\n\n\n\n<p>Realize your computer vision deployment needs on Intel\u00ae platforms\u2014from smart cameras and video surveillance to robotics, transportation, and much more. The <a href=\"https:\/\/software.intel.com\/en-us\/openvino-toolkit?utm_campaign=cmd_openvino&amp;utm_source=ibd&amp;utm_medium=synd&amp;utm_content=prod-info&amp;utm_term=hpc_ww&amp;cid=cmd_openvino_ibd_synd\">Intel\u00ae Distribution of OpenVINO\u2122 Toolkit<\/a> (includes the Intel\u00ae Deep Learning Deployment Toolkit) allows for the development of deep learning inference solutions for multiple platforms. The toolkit allows developers to build applications that emulate human vision, and is based on convolutional neural networks (CNNs), extending workloads across Intel\u00ae hardware (including accelerators) and maximizes performance in the following ways:<\/p>\n\n\n\n<ul><li>Enables\n     deep learning inference from edge to cloud.<\/li><li>Supports\n     heterogeneous execution across Intel platforms and accelerators\u2014CPU, GPU,\n     VPU, and FPGA\u2014using a common unified API.<\/li><li>Speeds\n     up time to market via a library of functions and pre-optimized kernels.<\/li><li>Provides extensibility and supports custom layer implementations.<\/li><li>Includes the Deep Learning Workbench, a GUI tool for running\n     inference experiments and determine optimal configurations. <\/li><li>Includes\n     optimized calls for OpenCV and OpenVX.<\/li><\/ul>\n\n\n\n<div class=\"wp-block-image\"><figure class=\"aligncenter\"><img decoding=\"async\" loading=\"lazy\" width=\"607\" height=\"335\" src=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/08\/OpenVINO_pic1.png\" alt=\"\" class=\"wp-image-23142\" srcset=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/08\/OpenVINO_pic1.png 607w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/08\/OpenVINO_pic1-150x83.png 150w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/08\/OpenVINO_pic1-300x166.png 300w\" sizes=\"(max-width: 607px) 100vw, 607px\" \/><figcaption>Computer vision capabilities even at the edge with OpenVINO\u2122<\/figcaption><\/figure><\/div>\n\n\n\n<p><strong>Improved Neural Network Performance<\/strong><\/p>\n\n\n\n<p>The OpenVINO\u2122 toolkit provides developers with improved neural network\nperformance on a variety of Intel\u00ae processors and helps further unlock\ncost-effective, real-time AI applications. The toolkit enables deep learning\ninference and straightforward heterogeneous execution across multiple Intel\u00ae\nplatforms (CPU, Intel\u00ae Processor Graphics)\u2014providing implementations across\ncloud architectures to edge devices. &nbsp;<\/p>\n\n\n\n<p>The OpenVINO\u2122 toolkit is an open-source product. It contains the <a href=\"https:\/\/github.com\/opencv\/dldt?utm_campaign=cmd_openvino&amp;utm_source=ibd&amp;utm_medium=synd&amp;utm_content=prod-info&amp;utm_term=hpc_ww&amp;cid=cmd_openvino_ibd_synd\">Deep Learning Deployment Toolkit (DLDT)<\/a> for Intel\u00ae processors (for CPUs), Intel\u00ae Processor Graphics (for GPUs), and heterogeneous support. It also includes an <a href=\"https:\/\/github.com\/opencv\/open_model_zoo?utm_campaign=cmd_openvino&amp;utm_source=ibd&amp;utm_medium=synd&amp;utm_content=prod-info&amp;utm_term=hpc_ww&amp;cid=cmd_openvino_ibd_synd\">open model zoo<\/a> with pre-trained models, samples, and demos.<\/p>\n\n\n\n<div class=\"wp-block-image\"><figure class=\"aligncenter\"><img decoding=\"async\" loading=\"lazy\" width=\"700\" height=\"231\" src=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/08\/OpenVINO-DL-training-pic.png\" alt=\"\" class=\"wp-image-23143\" srcset=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/08\/OpenVINO-DL-training-pic.png 700w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/08\/OpenVINO-DL-training-pic-150x50.png 150w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/08\/OpenVINO-DL-training-pic-300x99.png 300w\" sizes=\"(max-width: 700px) 100vw, 700px\" \/><figcaption>Optimized models use the Deep Learning Deployment Toolkit from Intel and the Intel\u00ae Math Kernel Library for Deep Neural Networks (Intel\u00ae MKL-DNN) to deliver outstanding inferencing performance for practical deployment of AI solutions at the \u201cedge\u201d of the enterprise in clinical and research settings<\/figcaption><\/figure><\/div>\n\n\n\n<p><strong>Reference Implementations<\/strong><\/p>\n\n\n\n<p>There are a\nnumber of compelling reference implementation for deploying OpenVINO\u2122. These\nimplementations afford data scientists and developers a significant head-start\nfor developing solutions in a number of different problem domains:<\/p>\n\n\n\n<ul><li><a href=\"https:\/\/www.youtube.com\/watch?v=FZZD4FCvO9c&amp;feature=youtu.be?utm_campaign=cmd_openvino&amp;utm_source=ibd&amp;utm_medium=synd&amp;utm_content=prod-info&amp;utm_term=hpc_ww&amp;cid=cmd_openvino_ibd_synd\">Vehicle\ndetection and license plate recognition<\/a>.<\/li><li><a href=\"https:\/\/github.com\/intel-iot-devkit\/people-counter-cpp?utm_campaign=cmd_openvino&amp;utm_source=ibd&amp;utm_medium=synd&amp;utm_content=prod-info&amp;utm_term=hpc_ww&amp;cid=cmd_openvino_ibd_synd\">People\ncounter systems<\/a> &#8211; this reference implementation is designed to detect people in a designated area and determine the\nnumber of people in the frame, the average time they are in the frame, and the\ntotal count.<\/li><li><a href=\"https:\/\/github.com\/intel-iot-devkit\/shopper-gaze-monitor-python?utm_campaign=cmd_openvino&amp;utm_source=ibd&amp;utm_medium=synd&amp;utm_content=prod-info&amp;utm_term=hpc_ww&amp;cid=cmd_openvino_ibd_synd\">Shopper\ngaze monitor<\/a> &#8211; this application uses the\nInference Engine included in OpenVINO\u2122 and the Intel\u00ae Deep Learning Deployment\nToolkit. It is designed for a retail shelf mounted camera system that counts\nthe number of passers-by that look towards the display vs. the number of people\nthat pass by the display without looking.<\/li><li><a href=\"https:\/\/github.com\/intel-iot-devkit\/intelligent-kiosk-analytics-cpp?utm_campaign=cmd_openvino&amp;utm_source=ibd&amp;utm_medium=synd&amp;utm_content=prod-info&amp;utm_term=hpc_ww&amp;cid=cmd_openvino_ibd_synd\">Digital\nkiosk display for 4K ads<\/a> &#8211; this\napplication identifies the age and gender of the audience standing in front of\ndigital signage, and based on the identification, it selects a suitable 4K\nadvertisement. <\/li><li><a href=\"https:\/\/github.com\/intel-iot-devkit\/store-aisle-monitor-python?utm_campaign=cmd_openvino&amp;utm_source=ibd&amp;utm_medium=synd&amp;utm_content=prod-info&amp;utm_term=hpc_ww&amp;cid=cmd_openvino_ibd_synd\">Store\nisle monitor<\/a> &#8211; this reference implementation\ncounts the number of people present in an image and generates a motion heatmap.\nIt takes the input from the camera, or a video file for processing. Snapshots\nof the output are taken at regular intervals and are uploaded to the cloud. It\nalso stores the snapshots of the output locally.<\/li><li><a href=\"https:\/\/github.com\/intel-iot-devkit\/store-traffic-monitor-python?utm_campaign=cmd_openvino&amp;utm_source=ibd&amp;utm_medium=synd&amp;utm_content=prod-info&amp;utm_term=hpc_ww&amp;cid=cmd_openvino_ibd_synd\">Store\ntraffic monitor<\/a> &#8211; this application is one of a\nseries of IoT reference implementations aimed at instructing data scientists\nand developers on how to develop a working solution for a particular problem.\nIt demonstrates how to create a smart video IoT solution using Intel\u00ae hardware\nand software tools. This reference implementation monitors people activity\ninside and outside a facility, as well as counting product inventory.<\/li><li><a href=\"https:\/\/github.com\/intel-iot-devkit\/safety-gear-detector-python?utm_campaign=cmd_openvino&amp;utm_source=ibd&amp;utm_medium=synd&amp;utm_content=prod-info&amp;utm_term=hpc_ww&amp;cid=cmd_openvino_ibd_synd\">Safety\ngear detector<\/a> &#8211; this application is an IoT\nreference implementation that detects people and potential violations of\nsafety-gear standards.<\/li><li><a href=\"https:\/\/github.com\/intel-iot-devkit\/restricted-zone-notifier-python?utm_campaign=cmd_openvino&amp;utm_source=ibd&amp;utm_medium=synd&amp;utm_content=prod-info&amp;utm_term=hpc_ww&amp;cid=cmd_openvino_ibd_synd\">Restricted\nzone notifier<\/a> &#8211; this application uses the\nInference Engine included in the Intel\u00ae Distribution of OpenVINO\u2122 toolkit and\nthe Intel\u00ae Deep Learning Deployment Toolkit. A trained neural network detects\npeople within a marked assembly area, which is designed for a machine mounted\ncamera system. It sends an alert if there is at least one person detected in\nthe marked assembly area. <\/li><li><a href=\"https:\/\/github.com\/intel-iot-devkit\/object-size-detector-python?utm_campaign=cmd_openvino&amp;utm_source=ibd&amp;utm_medium=synd&amp;utm_content=prod-info&amp;utm_term=hpc_ww&amp;cid=cmd_openvino_ibd_synd\">Object\nsize detection<\/a> &#8211; this application demonstrates\nhow to use computer vision to detect and measure the approximate length, width\nand size of assembly line parts. It is designed to work with an assembly line\ncamera mounted above the assembly line belt. The application monitors\nmechanical parts as they are moving down the assembly line and raises an alert\nif it detects a part on the belt outside a specified size range.<\/li><li><a href=\"https:\/\/github.com\/intel-iot-devkit\/intruder-detector-cpp?utm_campaign=cmd_openvino&amp;utm_source=ibd&amp;utm_medium=synd&amp;utm_content=prod-info&amp;utm_term=hpc_ww&amp;cid=cmd_openvino_ibd_synd\">Intruder\ndetector<\/a> &#8211; this application is an IoT\nreference implementation aimed at demonstrating how to create a smart video IoT\nsolution using Intel\u00ae hardware and software tools. This solution detects any\nnumber of objects in a designated area, providing the number of objects in the\nframe and total count.<\/li><\/ul>\n\n\n\n<p><strong>A Strategic Toolkit for Data\nScientists<\/strong><\/p>\n\n\n\n<p>The OpenVINO\u2122 toolkit enables CNN-based deep learning inference on the edge for\ncomputer vision applications. The toolkit\nis specifically designed for data scientists and software developers who work\non AI applications, computer vision, neural network inference, and deep\nlearning deployment capabilities. It is also for those who need to accelerate\ntheir solutions across multiple platforms including CPU, GPU, VPU, and FPGA. <\/p>\n","protected":false},"excerpt":{"rendered":"<p>Realize your computer vision deployment needs on Intel\u00ae platforms\u2014from smart cameras and video surveillance to robotics, transportation, and much more. The Intel\u00ae Distribution of OpenVINO\u2122 Toolkit (includes the Intel\u00ae Deep Learning Deployment Toolkit) allows for the development of deep learning inference solutions for multiple platforms. <\/p>\n","protected":false},"author":37,"featured_media":23145,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[526,87,180,210,773,67,56,1],"tags":[437,581,568,95],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Develop Multiplatform Computer Vision Solutions with Intel\u00ae Distribution of OpenVINO\u2122 Toolkit - insideBIGDATA<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/insidebigdata.com\/2019\/08\/26\/develop-multiplatform-computer-vision-solutions-with-intel-distribution-of-openvino-toolkit\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Develop Multiplatform Computer Vision Solutions with Intel\u00ae Distribution of OpenVINO\u2122 Toolkit - insideBIGDATA\" \/>\n<meta property=\"og:description\" content=\"Realize your computer vision deployment needs on Intel\u00ae platforms\u2014from smart cameras and video surveillance to robotics, transportation, and much more. 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Gutierrez is a Data Scientist with Los Angeles-based AMULET Analytics, a service division of AMULET Development Corp. He's been involved with data science and Big Data long before it came in vogue, so imagine his delight when the Harvard Business Review recently deemed \\\"data scientist\\\" as the sexiest profession for the 21st century. Previously, he taught computer science and database classes at UCLA Extension for over 15 years, and authored three computer industry books on database technology. He also served as technical editor, columnist and writer at a major computer industry monthly publication for 7 years. Follow his data science musings at @AMULETAnalytics.\",\"sameAs\":[\"http:\/\/www.insidebigdata.com\",\"https:\/\/twitter.com\/@AMULETAnalytics\"],\"url\":\"https:\/\/insidebigdata.com\/author\/dangutierrez\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Develop Multiplatform Computer Vision Solutions with Intel\u00ae Distribution of OpenVINO\u2122 Toolkit - insideBIGDATA","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/insidebigdata.com\/2019\/08\/26\/develop-multiplatform-computer-vision-solutions-with-intel-distribution-of-openvino-toolkit\/","og_locale":"en_US","og_type":"article","og_title":"Develop Multiplatform Computer Vision Solutions with Intel\u00ae Distribution of OpenVINO\u2122 Toolkit - insideBIGDATA","og_description":"Realize your computer vision deployment needs on Intel\u00ae platforms\u2014from smart cameras and video surveillance to robotics, transportation, and much more. The Intel\u00ae Distribution of OpenVINO\u2122 Toolkit (includes the Intel\u00ae Deep Learning Deployment Toolkit) allows for the development of deep learning inference solutions for multiple platforms.","og_url":"https:\/\/insidebigdata.com\/2019\/08\/26\/develop-multiplatform-computer-vision-solutions-with-intel-distribution-of-openvino-toolkit\/","og_site_name":"insideBIGDATA","article_publisher":"http:\/\/www.facebook.com\/insidebigdata","article_published_time":"2019-08-26T15:30:10+00:00","article_modified_time":"2019-08-27T15:35:23+00:00","og_image":[{"width":300,"height":166,"url":"https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/08\/Intel_CompVision-Hero1-650x360.jpg","type":"image\/jpeg"}],"author":"Daniel Gutierrez","twitter_card":"summary_large_image","twitter_creator":"@AMULETAnalytics","twitter_site":"@insideBigData","twitter_misc":{"Written by":"Daniel Gutierrez","Est. reading time":"4 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/insidebigdata.com\/2019\/08\/26\/develop-multiplatform-computer-vision-solutions-with-intel-distribution-of-openvino-toolkit\/","url":"https:\/\/insidebigdata.com\/2019\/08\/26\/develop-multiplatform-computer-vision-solutions-with-intel-distribution-of-openvino-toolkit\/","name":"Develop Multiplatform Computer Vision Solutions with Intel\u00ae Distribution of OpenVINO\u2122 Toolkit - insideBIGDATA","isPartOf":{"@id":"https:\/\/insidebigdata.com\/#website"},"datePublished":"2019-08-26T15:30:10+00:00","dateModified":"2019-08-27T15:35:23+00:00","author":{"@id":"https:\/\/insidebigdata.com\/#\/schema\/person\/2540da209c83a68f4f5922848f7376ed"},"breadcrumb":{"@id":"https:\/\/insidebigdata.com\/2019\/08\/26\/develop-multiplatform-computer-vision-solutions-with-intel-distribution-of-openvino-toolkit\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/insidebigdata.com\/2019\/08\/26\/develop-multiplatform-computer-vision-solutions-with-intel-distribution-of-openvino-toolkit\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/insidebigdata.com\/2019\/08\/26\/develop-multiplatform-computer-vision-solutions-with-intel-distribution-of-openvino-toolkit\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/insidebigdata.com\/"},{"@type":"ListItem","position":2,"name":"Develop Multiplatform Computer Vision Solutions with Intel\u00ae Distribution of OpenVINO\u2122 Toolkit"}]},{"@type":"WebSite","@id":"https:\/\/insidebigdata.com\/#website","url":"https:\/\/insidebigdata.com\/","name":"insideBIGDATA","description":"Your Source for AI, Data Science, Deep Learning &amp; Machine Learning Strategies","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/insidebigdata.com\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"en-US"},{"@type":"Person","@id":"https:\/\/insidebigdata.com\/#\/schema\/person\/2540da209c83a68f4f5922848f7376ed","name":"Daniel Gutierrez","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/insidebigdata.com\/#\/schema\/person\/image\/","url":"https:\/\/secure.gravatar.com\/avatar\/5780282e7e567e2a502233e948464542?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/5780282e7e567e2a502233e948464542?s=96&d=mm&r=g","caption":"Daniel Gutierrez"},"description":"Daniel D. Gutierrez is a Data Scientist with Los Angeles-based AMULET Analytics, a service division of AMULET Development Corp. He's been involved with data science and Big Data long before it came in vogue, so imagine his delight when the Harvard Business Review recently deemed \"data scientist\" as the sexiest profession for the 21st century. Previously, he taught computer science and database classes at UCLA Extension for over 15 years, and authored three computer industry books on database technology. He also served as technical editor, columnist and writer at a major computer industry monthly publication for 7 years. Follow his data science musings at @AMULETAnalytics.","sameAs":["http:\/\/www.insidebigdata.com","https:\/\/twitter.com\/@AMULETAnalytics"],"url":"https:\/\/insidebigdata.com\/author\/dangutierrez\/"}]}},"jetpack_featured_media_url":"https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/08\/Intel_CompVision-Hero1-650x360.jpg","jetpack_shortlink":"https:\/\/wp.me\/p9eA3j-61f","jetpack-related-posts":[{"id":18054,"url":"https:\/\/insidebigdata.com\/2017\/06\/08\/openvx-standard-computer-vision\/","url_meta":{"origin":23141,"position":0},"title":"OpenVX &#8211; The Standard for Computer Vision","date":"June 8, 2017","format":false,"excerpt":"OpenVX is an API enabling software developers to add hardware accelerated computer vision capabilities to their programs. Coupled with the current upswing in the use of deep learning technologies, computer vision applications with OpenVX are becoming very important. OpenVX is an integral part of Intel Computer Vision SDK. This comprehensive\u2026","rel":"","context":"In &quot;AI Deep Learning&quot;","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2017\/06\/12730-4_INTEL_DPD_VCP_OpenVX_Infographic_v5.0.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":23824,"url":"https:\/\/insidebigdata.com\/2020\/01\/13\/oneapi-a-unified-cross-architecture-high-performance-programming-model-designed-to-help-shape-the-future-of-application-development\/","url_meta":{"origin":23141,"position":1},"title":"oneAPI: &#8211; A Unified Cross-Architecture, High Performance Programming Model Designed to Help Shape the Future of Application Development","date":"January 13, 2020","format":false,"excerpt":"In this article, we\u2019ll dive into the newly announced oneAPI, a single, unified programming model that aims to simplify development across multiple architectures, such as CPUs, GPUs, FPGAs and other accelerators. The long-term journey is represented by two important first-steps \u2013 the industry initiative and the Intel beta product.","rel":"","context":"In &quot;Featured&quot;","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2020\/01\/oneAPI_toolkit.jpg?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":33473,"url":"https:\/\/insidebigdata.com\/2023\/09\/20\/intel-innovation-2023-highlights\/","url_meta":{"origin":23141,"position":2},"title":"Intel Innovation 2023 Highlights","date":"September 20, 2023","format":false,"excerpt":"Tuesday morning (Sept. 19, 2023), Intel kicked off its third annual developer event, Intel Innovation 2023, virtually and in San Jose, California. During the Day 1 keynote, \u201cDeveloping the Future of the Siliconomy,\u201d Intel CEO Pat Gelsinger, and a variety of customers, unveiled an array of technologies and applications that\u2026","rel":"","context":"In &quot;AI Deep Learning&quot;","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2023\/09\/AI_data_storage_shutterstock_1107715973_special.jpg?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":28941,"url":"https:\/\/insidebigdata.com\/2022\/04\/06\/deci-boosts-computer-vision-nlp-models-performance-at-mlperf\/","url_meta":{"origin":23141,"position":3},"title":"Deci Boosts Computer Vision &#038; NLP Models\u2019 Performance at MLPerf\u00a0","date":"April 6, 2022","format":false,"excerpt":"Deci, the deep learning company harnessing Artificial Intelligence (AI) to build AI, announced its results for both Computer Vision (CV) and Natural Language Processing (NLP) inference models that were submitted to the MLPerf v2.0\u00a0Datacenter Open division. These submissions demonstrated the power of Deci\u2019s Automated Neural Architecture Construction (AutoNAC) technology, which\u2026","rel":"","context":"In &quot;AI Deep Learning&quot;","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":24627,"url":"https:\/\/insidebigdata.com\/2020\/06\/18\/intel-announces-unmatched-ai-and-analytics-platform-with-new-processor-memory-storage-and-fpga-solutions\/","url_meta":{"origin":23141,"position":4},"title":"Intel Announces AI and Analytics Platform with New Processor, Memory, Storage and FPGA Solutions","date":"June 18, 2020","format":false,"excerpt":"Intel today introduced its 3rd Gen Intel Xeon Scalable processors and additions to its hardware and software AI portfolio, enabling customers to accelerate the development and use of AI and analytics workloads running in data center, network and intelligent-edge environments. As the industry\u2019s first mainstream server processor with built-in bfloat16\u2026","rel":"","context":"In &quot;AI Deep Learning&quot;","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2016\/08\/Intel_updated-logo.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":31488,"url":"https:\/\/insidebigdata.com\/2023\/01\/28\/deci-delivers-breakthrough-inference-performance-on-intels-4th-gen-sapphire-rapids-cpu\/","url_meta":{"origin":23141,"position":5},"title":"Deci delivers breakthrough inference performance on Intel&#8217;s 4th Gen Sapphire Rapids CPU","date":"January 28, 2023","format":false,"excerpt":"Deci,\u00a0the deep learning company building the next generation of AI,\u00a0announced\u00a0a\u00a0breakthrough performance on Intel\u2019s newly released 4th\u00a0Gen Intel\u00ae Xeon\u00ae Scalable processors, code-named\u00a0Sapphire Rapids. By optimizing the AI models which run on Intel\u2019s new hardware, Deci enables AI developers to achieve GPU-like inference performance on CPUs in production for both Computer Vision\u2026","rel":"","context":"In &quot;AI Deep Learning&quot;","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2023\/01\/Deci_fig1.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]}],"_links":{"self":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts\/23141"}],"collection":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/users\/37"}],"replies":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/comments?post=23141"}],"version-history":[{"count":0,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts\/23141\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/media\/23145"}],"wp:attachment":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/media?parent=23141"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/categories?post=23141"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/tags?post=23141"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}