{"id":26460,"date":"2025-05-26T10:35:37","date_gmt":"2025-05-26T08:35:37","guid":{"rendered":"https:\/\/monraspberry.com\/?post_type=product&#038;p=26460"},"modified":"2026-06-04T17:18:18","modified_gmt":"2026-06-04T15:18:18","slug":"google-coral-m-2-ae-accelerator","status":"publish","type":"product","link":"https:\/\/monraspberry.com\/en\/produit\/accelerateur-google-coral-m-2-ae\/","title":{"rendered":"Google Coral M.2 gas pedal (A+E)"},"content":{"rendered":"<p><span class=\"relative -mx-px my-[-0.2rem] rounded px-px py-[0.2rem] transition-colors duration-100 ease-in-out\">Le <strong data-start=\"3\" data-end=\"43\">Google Coral M.2 Accelerator A+E Key<\/strong> est un module d&#8217;acc\u00e9l\u00e9ration AI\/ML compact, con\u00e7u pour int\u00e9grer le coprocesseur Edge TPU de Google dans des syst\u00e8mes embarqu\u00e9s via une interface M.2 A+E Key.<\/span> <span class=\"relative -mx-px my-[-0.2rem] rounded px-px py-[0.2rem] transition-colors duration-100 ease-in-out\">Ce module offre une acc\u00e9l\u00e9ration mat\u00e9rielle pour les mod\u00e8les TensorFlow Lite, permettant des inf\u00e9rences rapides et efficaces directement sur le p\u00e9riph\u00e9rique.<\/span><\/p>\n<h2 data-start=\"448\" data-end=\"496\">Caract\u00e9ristiques techniques principales :<\/h2>\n<ul>\n<li data-start=\"500\" data-end=\"561\"><strong data-start=\"500\" data-end=\"519\">Coprocesseur ML<\/strong> : <span class=\"relative -mx-px my-[-0.2rem] rounded px-px py-[0.2rem] transition-colors duration-100 ease-in-out\">Google Edge TPU, capable d&#8217;effectuer jusqu&#8217;\u00e0 4 TOPS (op\u00e9rations par seconde) en pr\u00e9cision int8, avec une efficacit\u00e9 \u00e9nerg\u00e9tique de 2 TOPS par watt.<\/span><\/li>\n<li data-start=\"564\" data-end=\"619\"><strong data-start=\"564\" data-end=\"577\">Interface<\/strong> : <span class=\"relative -mx-px my-[-0.2rem] rounded px-px py-[0.2rem] transition-colors duration-100 ease-in-out\">M.2 A+E Key (M.2-2230-A-E-S3) via PCIe Gen2 x1.<\/span><\/li>\n<li data-start=\"622\" data-end=\"678\"><strong data-start=\"622\" data-end=\"636\">Dimensions<\/strong> : <span class=\"relative -mx-px my-[-0.2rem] rounded px-px py-[0.2rem] transition-colors duration-100 ease-in-out\">22 mm x 30 mm.<\/span><\/li>\n<li data-start=\"681\" data-end=\"739\"><strong data-start=\"681\" data-end=\"697\">Alimentation<\/strong> : <span class=\"relative -mx-px my-[-0.2rem] rounded px-px py-[0.2rem] transition-colors duration-100 ease-in-out\">3,3 V via PCIe.<\/span><\/li>\n<li data-start=\"742\" data-end=\"817\"><strong data-start=\"742\" data-end=\"775\">Temp\u00e9rature de fonctionnement<\/strong> : <span class=\"relative -mx-px my-[-0.2rem] rounded px-px py-[0.2rem] transition-colors duration-100 ease-in-out\">-20 \u00e0 +85 \u00b0C.<\/span><\/li>\n<li data-start=\"820\" data-end=\"929\"><strong data-start=\"820\" data-end=\"848\">Compatibilit\u00e9 logicielle<\/strong> : <span class=\"relative -mx-px my-[-0.2rem] rounded px-px py-[0.2rem] transition-colors duration-100 ease-in-out\">Supporte TensorFlow Lite pour l&#8217;ex\u00e9cution de mod\u00e8les ML optimis\u00e9s.<\/span><\/li>\n<\/ul>\n<h2 data-start=\"931\" data-end=\"962\">Applications typiques :<\/h2>\n<ul>\n<li data-start=\"966\" data-end=\"1005\"><span class=\"relative -mx-px my-[-0.2rem] rounded px-px py-[0.2rem] transition-colors duration-100 ease-in-out\">Int\u00e9gration dans des syst\u00e8mes embarqu\u00e9s pour l&#8217;acc\u00e9l\u00e9ration AI\/ML.<\/span><\/li>\n<li data-start=\"1008\" data-end=\"1047\"><span class=\"relative -mx-px my-[-0.2rem] rounded px-px py-[0.2rem] transition-colors duration-100 ease-in-out\">Utilisation dans des dispositifs IoT n\u00e9cessitant des capacit\u00e9s d&#8217;inf\u00e9rence locales.<\/span><\/li>\n<li data-start=\"1050\" data-end=\"1128\"><span class=\"relative -mx-px my-[-0.2rem] rounded px-px py-[0.2rem] transition-colors duration-100 ease-in-out\">D\u00e9veloppement de solutions AI\/ML sur des plateformes avec interface M.2 A+E Key.<\/span><\/li>\n<\/ul>\n<p data-start=\"1130\" data-end=\"1208\"><span class=\"relative -mx-px my-[-0.2rem] rounded px-px py-[0.2rem] transition-colors duration-100 ease-in-out\">Ce module est id\u00e9al pour les d\u00e9veloppeurs souhaitant ajouter des capacit\u00e9s d&#8217;acc\u00e9l\u00e9ration AI\/ML \u00e0 leurs syst\u00e8mes embarqu\u00e9s, offrant une solution compacte et \u00e9conome en \u00e9nergie.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<ul>\n<li data-start=\"1253\" data-end=\"1292\"><span class=\"relative -mx-px my-[-0.2rem] rounded px-px py-[0.2rem] transition-colors duration-100 ease-in-out\">Coprocesseur Edge TPU de Google pour acc\u00e9l\u00e9ration AI\/ML.<\/span><\/li>\n<li data-start=\"1295\" data-end=\"1334\"><span class=\"relative -mx-px my-[-0.2rem] rounded px-px py-[0.2rem] transition-colors duration-100 ease-in-out\">Interface M.2 A+E Key via PCIe Gen2 x1.<\/span><\/li>\n<li data-start=\"1337\" data-end=\"1415\"><span class=\"relative -mx-px my-[-0.2rem] rounded px-px py-[0.2rem] transition-colors duration-100 ease-in-out\">Supporte TensorFlow Lite pour l&#8217;ex\u00e9cution de mod\u00e8les ML optimis\u00e9s.<\/span><\/li>\n<\/ul>\n","protected":false},"featured_media":26461,"template":"","meta":[],"product_brand":[393],"product_cat":[20],"product_tag":[],"class_list":["post-26460","product","type-product","status-publish","has-post-thumbnail","product_brand-raspberry-pi","product_cat-accessoires-raspberry-pi-5","first","instock","taxable","shipping-taxable","purchasable","product-type-simple","add-to-wishlist-after_add_to_cart"],"jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/monraspberry.com\/en\/wp-json\/wp\/v2\/product\/26460","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/monraspberry.com\/en\/wp-json\/wp\/v2\/product"}],"about":[{"href":"https:\/\/monraspberry.com\/en\/wp-json\/wp\/v2\/types\/product"}],"version-history":[{"count":0,"href":"https:\/\/monraspberry.com\/en\/wp-json\/wp\/v2\/product\/26460\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/monraspberry.com\/en\/wp-json\/wp\/v2\/media\/26461"}],"wp:attachment":[{"href":"https:\/\/monraspberry.com\/en\/wp-json\/wp\/v2\/media?parent=26460"}],"wp:term":[{"taxonomy":"product_brand","embeddable":true,"href":"https:\/\/monraspberry.com\/en\/wp-json\/wp\/v2\/product_brand?post=26460"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/monraspberry.com\/en\/wp-json\/wp\/v2\/product_cat?post=26460"},{"taxonomy":"product_tag","embeddable":true,"href":"https:\/\/monraspberry.com\/en\/wp-json\/wp\/v2\/product_tag?post=26460"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}