{"id":129938,"date":"2018-02-16T09:25:28","date_gmt":"2018-02-16T09:25:28","guid":{"rendered":"https:\/\/\/arm-lance-deux-processeurs-dapprentissage-automatique\/"},"modified":"2018-02-16T09:25:28","modified_gmt":"2018-02-16T09:25:28","slug":"arm-lance-deux-processeurs-dapprentissage-automatique","status":"publish","type":"post","link":"https:\/\/www.ecinews.fr\/fr\/arm-lance-deux-processeurs-dapprentissage-automatique\/","title":{"rendered":"ARM lance deux processeurs d&rsquo;apprentissage automatique"},"content":{"rendered":"<p><span id=\"result_box\" lang=\"fr\"><span>Les processeurs sont l&rsquo;ARM ML ( ML pour machine learning) et l&rsquo; ARM OD avec OD pour la d\u00e9tection d&rsquo;objets.<\/span> <span>Ces appareils permettront des trillions d&rsquo;op\u00e9rations par seconde et sont destin\u00e9s \u00e0 \u00eatre utilis\u00e9s sur des p\u00e9riph\u00e9riques tels que les t\u00e9l\u00e9phones portables.<\/span> <span>ARM affirme que le processeur ML est la \u00absolution la plus efficace pour d\u00e9velopper des r\u00e9seaux de neurones\u00bb.<\/span><\/span><\/p>\n<p><span id=\"result_box\" lang=\"fr\"><span>L&rsquo;ARM ML comprend des unit\u00e9s de calcul (engine) \u00e0 fonction fixe et des unit\u00e9s de calcul \u00e0 couches programmables pour des op\u00e9rations primitives s\u00e9lectionn\u00e9es, tout en permettant l&rsquo;innovation et des algorithmes futurs<\/span><\/span><span lang=\"fr\"><span>.<\/span> <span>Une unit\u00e9 de contr\u00f4le de r\u00e9seau g\u00e8re l&rsquo;ex\u00e9cution globale du r\u00e9seau neuronal et une unit\u00e9 DMA d\u00e9place les donn\u00e9es dans et hors de la m\u00e9moire principale.<\/span> <span>La m\u00e9moire int\u00e9gr\u00e9e permet un stockage centralis\u00e9 pour les poids et les cartes de fonctions, r\u00e9duisant ainsi le trafic vers la m\u00e9moire externe et, par cons\u00e9quent, la puissance.<\/span><\/span><\/p>\n<p>&nbsp;<\/p>\n<p><img decoding=\"async\" alt=\"\" height=\"502\" data-src=\"http:\/\/www.eenewseurope.com\/sites\/default\/files\/images\/01-picture-library\/PeterClarke\/2018\/02\/armmlprocessor.jpg\" width=\"530\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" class=\"lazyload\" style=\"--smush-placeholder-width: 530px; --smush-placeholder-aspect-ratio: 530\/502;\" \/><\/p>\n<p><span id=\"result_box\" lang=\"fr\"><strong><span title=\"Not much detail yet in ARM's description of its ML processor.\">ARM ne donne pas encore beaucoup de d\u00e9tails dans la description de son processeur ML. <\/span><span title=\"Source: ARM\n\n\">Source: ARM<\/span><\/strong><\/p>\n<p><span title=\"The performance is said to be greater than 4.6TOPS in mobile environments with an efficiency of 3TOPS per watt.\">La performance serait sup\u00e9rieure \u00e0 4.6TOPS dans des environnements mobiles avec une efficacit\u00e9 de 3TOPS par watt. <\/span><span title=\"It is also said to provide a &quot;massive efficiency uplift from CPUs, GPUs, DSPs and accelerators.&quot;\n\n\">Il est \u00e9galement dit fournir un efficacit\u00e9 massivement sup\u00e9rieure aux processeurs,CPU, GPU, DSP et acc\u00e9l\u00e9rateurs.\u00a0\u00bb<\/span><\/p>\n<p><span title=\"However, while the design is said to be tuned for advanced geometry implementation, ARM does not indicate whether the IP has already been licensed to lead partners.\">Cependant, bien que la conception soit destin\u00e9e \u00e0 une impl\u00e9mentation en g\u00e9om\u00e9trie avanc\u00e9e, ARM n&rsquo;indique pas si l&rsquo;IP a d\u00e9j\u00e0 \u00e9t\u00e9 donn\u00e9e en licence \u00e0 ses partenaires. <\/span><span title=\"Also ARM does not indicate more precisely the nature of the programmable and fixed-function engines or what data types are supported.\">De plus, ARM n&rsquo;indique pas plus pr\u00e9cis\u00e9ment la nature des unit\u00e9s de calcul programmables et \u00e0 fonction fixe ni quelles types de donn\u00e9es sont support\u00e9es.<\/span><\/span><\/p>\n<p>&nbsp;<\/p>\n<p><em><strong>A suivre: OD<\/strong><\/em><\/p>\n<hr \/>\n<p><span id=\"result_box\" lang=\"fr\"><span title=\"The ML processor can be used stand alone but also can be used with the OD processor, which is described as ARM's second generation of Object Detection processor.\">Le processeur ML peut \u00eatre utilis\u00e9 seul, mais peut \u00e9galement \u00eatre utilis\u00e9 avec le processeur OD, qui est pr\u00e9sent\u00e9 comme la deuxi\u00e8me g\u00e9n\u00e9ration de processeur de d\u00e9tection d&rsquo;objet d&rsquo;ARM. <\/span><span title=\"This is designed to work with 2D fields and with visual fields in particular.\n\n\">Il est con\u00e7u pour fonctionner avec des champs 2D et avec des champs visuels en particulier.<\/span><\/span><\/p>\n<p><span id=\"result_box\" lang=\"fr\"><span title=\"The OD processor scans each frame at 60fps and provides a list of detected objects, along with their location within the scene.\">Le processeur OD analyse chaque image \u00e0 60 fps et fournit une liste des objets d\u00e9tect\u00e9s, ainsi que leur emplacement dans la sc\u00e8ne. <\/span><span title=\"The devices detects human forms, faces, heads and shoulders, and can even determine the direction each person is facing.\">Les OD d\u00e9tectent les formes humaines, les visages, les t\u00eates et les \u00e9paules, et peuvent m\u00eame d\u00e9terminer la direction de chaque personne. <\/span><span title=\"Object sizes detected can be as small as 50 by 60 pixels.\n\n\">Les tailles d&rsquo;objet d\u00e9tect\u00e9es peuvent \u00eatre aussi petites que 50 par 60 pixels.<\/span><\/span><\/p>\n<p><span id=\"result_box\" lang=\"fr\"><span title=\"ARM claims the OD processor offers 80x the performance of a traditional DSP, and a significant improvement in detection quality relative to previous Arm technologies.\n\n\">ARM affirme que le processeur OD offre 80 fois les performances d&rsquo;un processeur DSP traditionnel et une am\u00e9lioration significative de la qualit\u00e9 de d\u00e9tection par rapport aux technologies pr\u00e9c\u00e9dentes.<\/span><\/p>\n<p><span title=\"The OD processor is intended to be used as a pre-processor to detect regions of interest \u2013 and particularly people of interest \u2013 and it can be used with ARM Cortex CPUs, Mali GPUs and the ML processor.\">Le processeur OD est destin\u00e9 \u00e0 \u00eatre utilis\u00e9 comme pr\u00e9-processeur pour d\u00e9tecter les r\u00e9gions d&rsquo;int\u00e9r\u00eat &#8211; et en particulier les personnes d&rsquo;int\u00e9r\u00eat &#8211; et il peut \u00eatre utilis\u00e9 avec les CPU ARM Cortex, les GPU Mali et le processeur ML.<\/span><\/span><\/p>\n<p><span id=\"result_box\" lang=\"fr\"><span title=\"The ML and OD processors can be deployed together or separately but they also can make use of ARM NN software and the ARM Compute Library\">Les processeurs ML et OD peuvent \u00eatre d\u00e9ploy\u00e9s ensemble ou s\u00e9par\u00e9ment, mais ils peuvent \u00e9galement utiliser le logiciel ARM NN et la biblioth\u00e8que ARM Compute<\/span><\/span> (voir <a href=\"http:\/\/www.electronics-eetimes.com\/news\/arms-soft-launch-machine-learning-library\">ARM&rsquo;s soft launch for machine learning library<\/a>).<\/p>\n<p>&nbsp;<\/p>\n<p><img decoding=\"async\" alt=\"\" height=\"267\" data-src=\"http:\/\/www.eenewseurope.com\/sites\/default\/files\/images\/01-picture-library\/PeterClarke\/2018\/02\/armnnsoft530.jpg\" width=\"530\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" class=\"lazyload\" style=\"--smush-placeholder-width: 530px; --smush-placeholder-aspect-ratio: 530\/267;\" \/><\/p>\n<p><strong><span id=\"result_box\" lang=\"fr\"><span title=\"Roadmap for ARM NN software as it bridges from TensorFlow, Caffe etc. to various processors.\">Feuille de route pour le logiciel ARM NN qui relie TensorFlow, Caffe etc. \u00e0 divers processeurs. <\/span><span title=\"Source: ARM\n\n\">Source: ARM<\/span><\/span><\/strong><br \/>\n&nbsp;<\/p>\n<p><span lang=\"fr\"><span title=\"ARM NN software, when used alongside the ARM Compute Library and CMSIS-NN is optimized for NNs and bridges the gap between NN frameworks such as TensorFlow, Caffe, and Android NN and the full range of Cortex CPUs, Mali GPUs, and ML processors.\n\n\">Le logiciel ARM NN (Neural Network) associ\u00e9 \u00e0 ARM Compute Library et CMSIS-NN, est optimis\u00e9 pour les NNs et comble le foss\u00e9 entre les frameworks NNs tels que TensorFlow, Caffe et Android NN et la gamme compl\u00e8te de processeurs Cortex, GPU Mali et processeurs ML.<\/span><\/span><\/p>\n<p><span id=\"result_box\" lang=\"fr\"><span title=\"Jem Davies, general manager of machine learning business at ARM, said that the advent of machine learning represents &quot;the biggest inflection point in computing for more than a generation.&quot;\">Jem Davies, directeur g\u00e9n\u00e9ral de l&rsquo;activit\u00e9 d&rsquo;apprentissage automatique chez ARM, a d\u00e9clar\u00e9 que l&rsquo;av\u00e8nement de l&rsquo;apprentissage automatique repr\u00e9sente \u00able plus grand point d&rsquo;inflexion de l&rsquo;informatique depuis plus d&rsquo;une g\u00e9n\u00e9ration\u00bb. <\/span><span title=\"He added that it will be done at the edge rather than in data centers wherever possible, for reasons of energy efficiency, latency, safety-criticality, economics and privacy.\n\n\">Il a ajout\u00e9 que cela sera fait \u00e0 la p\u00e9riph\u00e9rie plut\u00f4t que dans les centres de donn\u00e9es, dans la mesure du possible, pour des raisons d&rsquo;efficacit\u00e9 \u00e9nerg\u00e9tique, de latence, de s\u00e9curit\u00e9-criticit\u00e9, d&rsquo;\u00e9conomie et de confidentialit\u00e9.<\/span><\/span><\/p>\n<p><span id=\"result_box\" lang=\"fr\"><span title=\"ARM added that future ML products will enable developers to pick their point on a performance curve from sensors and smart speakers, to mobile, home entertainment, and beyond.\n\n\">ARM a ajout\u00e9 que les futurs produits ML permettront aux d\u00e9veloppeurs de choisir leur point id\u00e9al sur une courbe de performance allant des capteurs et haut-parleurs intelligents, au mobile, au divertissement \u00e0 domicile, et bien plus encore.<\/span><\/p>\n<p><span title=\"ARM stated that the ARM machine learning IP suite will be available for general availability in mid-2018.\">ARM a d\u00e9clar\u00e9 que la suite IP d&rsquo;apprentissage automatique d&rsquo;ARM sera pr\u00eate pour une disponibilit\u00e9 g\u00e9n\u00e9rale \u00e0 la mi-2018.<\/span><\/span><\/p>\n<p><a href=\"http:\/\/www.arm.com\">www.arm.com<\/a><\/p>\n<p><a href=\"https:\/\/developer.arm.com\/technologies\/machine-learning-on-arm\" target=\"_blank\" title=\"Machine Learning Developer Portal\" rel=\"noopener\">Machine Learning Developer Portal<\/a><\/p>\n<p><strong>La r\u00e9daction vous conseille aussi:<\/strong><\/p>\n<p><a href=\"http:\/\/www.electronique-eci.com\/news\/daimler-teste-lia-automobile-sur-5-continents\"><strong>Daimler teste l&rsquo;IA automobile sur 5 continents<\/strong><\/a><\/p>\n<p><a href=\"http:\/\/www.electronique-eci.com\/news\/quand-lia-saventure-dans-le-labyrinthe-de-la-cyber-securite\"><strong>Quand l&rsquo;IA s&rsquo;aventure dans le labyrinthe de la cyber-s\u00e9curit\u00e9<\/strong><\/a><\/p>\n<p><a href=\"http:\/\/www.electronique-eci.com\/news\/5g-iiot-le-rapport-ni-trend-watch-explore-les-tendances-de-lindustrie-pour-2018\"><strong>5G, IIoT : le rapport NI Trend Watch explore les tendances de l\u2019industrie pour 2018<\/strong><\/a><\/p>\n<p>&nbsp;<\/p>\n<p><strong>Related News articles in English: <\/strong><\/p>\n<p><a href=\"http:\/\/www.eenewsanalog.com\/news\/thoughts-jem-davies-leading-arms-machine-learning-group\">Thoughts on Jem Davies leading ARM&rsquo;s machine learning group<\/a><\/p>\n<p><a href=\"http:\/\/www.analog-eetimes.com\/news\/arm-has-rd-interest-neural-network-cores\">ARM has R&amp;D interest in neural network cores<\/a><\/p>\n<p><a href=\"http:\/\/www.electronics-eetimes.com\/news\/arms-soft-launch-machine-learning-library\">ARM&rsquo;s soft launch for machine learning library<\/a><\/p>\n<p><a href=\"http:\/\/www.eenewsanalog.com\/news\/arm-acquires-chaologix-security-reasons\">ARM acquires ChaoLogix for security reasons<\/a><\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Pr\u00e9sent\u00e9 comme un point d&rsquo;inflexion majeur de l&rsquo;informatique, ARM a annonc\u00e9 le d\u00e9veloppement de deux processeurs d&rsquo;apprentissage automatique sous le nom de code Project Trillium, qui inclut \u00e9galement des IP et des logiciels auxiliaires.<\/p>\n","protected":false},"author":22,"featured_media":129939,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[883],"tags":[905,906,917],"domains":[47],"ppma_author":[1149],"class_list":["post-129938","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-technologies","tag-memory-data-storage-fr","tag-mpus-mcus-fr","tag-software-embedded-tools-fr","domains-electronique-eci"],"acf":[],"yoast_head":"<title>ARM lance deux processeurs d&#039;apprentissage automatique ...<\/title>\n<meta name=\"description\" content=\"Pr\u00e9sent\u00e9 comme un point d&#039;inflexion majeur de l&#039;informatique, ARM a annonc\u00e9 le d\u00e9veloppement de deux processeurs d&#039;apprentissage automatique sous le nom...\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link 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