{"id":145376,"date":"2025-10-16T23:11:55","date_gmt":"2025-10-16T21:11:57","guid":{"rendered":"https:\/\/prohoster.info\/blog\/novosti-interneta\/google-predstavil-coral-npu-otkrytuyu-platformu-dlya-sozdaniya-ai-uskoritelej"},"modified":"2025-10-16T23:11:55","modified_gmt":"2025-10-16T21:11:57","slug":"google-predstavil-coral-npu-otkrytuyu-platformu-dlya-sozdaniya-ai-uskoritelej","status":"publish","type":"post","link":"https:\/\/prohoster.info\/hu\/blog\/news\/google-predstavil-coral-npu-otkrytuyu-platformu-dlya-sozdaniya-ai-uskoritelej","title":{"rendered":"A Google bemutatta a Coral NPU-t, egy ny\u00edlt platformot mesters\u00e9ges intelligencia gyors\u00edt\u00f3k l\u00e9trehoz\u00e1s\u00e1hoz.","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>A Google bemutatta a Coral NPU (Neur\u00e1lis Feldolgoz\u00f3 Egys\u00e9g) ny\u00edlt platformj\u00e1t, amely egy nyitott hardveres gyors\u00edt\u00f3t k\u00edn\u00e1l a g\u00e9pi tanul\u00e1si modellekhez, \u00e9s szoftveres eszk\u00f6zk\u00e9szletet ny\u00fajt a tipikus AI-motorokhoz val\u00f3 haszn\u00e1lathoz. A Coral alapk\u00e9nt szolg\u00e1lhat energiatakar\u00e9kos rendszerek fejleszt\u00e9s\u00e9hez, mint p\u00e9ld\u00e1ul a rendszer a chipben (SoC), amelyek az IoT, a Edge sz\u00e1m\u00edt\u00e1stechnika \u00e9s \u00e9rz\u00e9kel\u0151k adatgy\u0171jt\u0151 t\u00e1bl\u00e1k keretein bel\u00fcl alkalmazhat\u00f3k, tov\u00e1bb\u00e1 fogyaszt\u00f3i hordhat\u00f3 eszk\u00f6z\u00f6k eset\u00e9n, mint p\u00e9ld\u00e1ul fejhallgat\u00f3k, kiterjesztett val\u00f3s\u00e1g szem\u00fcvegek \u00e9s okos\u00f3r\u00e1k. A projekt eredm\u00e9nyei Apache 2.0 licenc alatt \u00e9rhet\u0151k el.    <\/p>\n<p>A Coral NPU c\u00e9lja, hogy a hordozhat\u00f3 eszk\u00f6z\u00f6k\u00f6n folyamatosan m\u0171k\u00f6d\u0151 AI-alkalmaz\u00e1sokat hajtson v\u00e9gre, minim\u00e1lis energiafelhaszn\u00e1l\u00e1ssal. A Coral NPU alapvet\u0151 megval\u00f3s\u00edt\u00e1sa 512 milli\u00e1rd m\u0171veletet biztos\u00edt m\u00e1sodpercenk\u00e9nt (GOPS), mind\u00f6ssze n\u00e9h\u00e1ny milliwatt energiafogyaszt\u00e1s mellett. Az NPU rugalmas architekt\u00far\u00e1ra lett tervezve, amely alkalmazkodik a SoC gy\u00e1rt\u00f3k ig\u00e9nyeihez. Az els\u0151 gy\u00e1rt\u00f3, amely a Coral NPU alap\u00fa chipjeit kezdi el el\u0151\u00e1ll\u00edtani, a Synaptics lesz, amely bejelentette IoT eszk\u00f6z\u00f6kh\u00f6z sz\u00e1nt Astra SL2610 processzorsorozat\u00e1t, ami mag\u00e1ban foglalja a Coral NPU architekt\u00far\u00e1ra alapozott Torq NPU alrendszert.            <\/p>\n<p>A Coral NPU tipikus alkalmaz\u00e1sai k\u00f6z\u00f6tt szerepel az AI felhaszn\u00e1l\u00e1sa k\u00e9pek \u00e9s hangok feldolgoz\u00e1s\u00e1hoz, felhaszn\u00e1l\u00f3i interakci\u00f3hoz \u00e9s kontextusok figyelembev\u00e9tel\u00e9hez. P\u00e9ld\u00e1ul az eszk\u00f6z\u00f6k k\u00e9pesek futtatni nagy nyelvi modelleket \u00e9s alkalmaz\u00e1sokat arcfelismer\u00e9sre, objektumok azonos\u00edt\u00e1s\u00e1ra, vizu\u00e1lis keres\u00e9sre, besz\u00e9dfelismer\u00e9sre, \u00e9l\u0151 ford\u00edt\u00e1sra, besz\u00e9d \u00e1tkonvert\u00e1l\u00e1s\u00e1ra, kulcsszavak kiemel\u00e9s\u00e9re, gesztus- \u00e9s hangutas\u00edt\u00e1sok kezel\u00e9s\u00e9re, tov\u00e1bb\u00e1 a felhaszn\u00e1l\u00f3 aktivit\u00e1s\u00e1nak (j\u00e1r\u00e1s, fut\u00e1s, alv\u00e1s) \u00e9s a k\u00f6rnyezet t\u00edpus\u00e1nak (otthon, szabadban) meghat\u00e1roz\u00e1s\u00e1ra.       <\/p>\n<p>Az NPU 32 bites RISC-V RV32IMF_Zve32x utas\u00edt\u00e1sk\u00e9szlet architekt\u00far\u00e1val, AXI4 busz-rendszerrel, n\u00e9gy l\u00e9pcs\u0151s utas\u00edt\u00e1s feldolgoz\u00f3 cs\u0151vezet\u00e9kkel, rendezett \u00fctemez\u00e9ssel, rendezetlen utas\u00edt\u00e1s v\u00e9grehajt\u00e1ssal, n\u00e9gy sz\u00e1l\u00fa sk\u00e1l\u00e1rral \u00e9s k\u00e9t sz\u00e1l\u00fa vektori\u00e1lis \u00fctemez\u00e9ssel m\u0171k\u00f6dik. A processzor t\u00e1mogatja a SIMD m\u0171veleteket 128 bites vektorok p\u00e1rhuzamos feldolgoz\u00e1s\u00e1hoz, \u00e9s 8 KB ITCM mem\u00f3ri\u00e1val rendelkezik az utas\u00edt\u00e1sokhoz, valamint 32 KB DTCM mem\u00f3ri\u00e1val az adatokhoz.     <center><img decoding=\"async\" alt=\"A Google bemutatta a Coral NPU-t, egy ny\u00edlt platformot mesters\u00e9ges intelligencia gyors\u00edt\u00f3k l\u00e9trehoz\u00e1s\u00e1hoz. \" src=\"\/wp-content\/uploads\/2025\/10\/beddd87f81c9a7fd544c28f9148e1c88.png\" style=\"display:block;margin: 0 auto;\" \/><\/center>    <\/p>\n<p>Az NPU h\u00e1rom egy\u00fcttm\u0171k\u00f6d\u0151 processzor-komponenst tartalmaz:  <\/p>\n<ul>\n<li class=\"l\"> A sk\u00e1l\u00e1ris sz\u00e1m\u00edt\u00e1sokhoz k\u00e9sz\u00fclt mag &#8212; egy k\u00f6nny\u0171, C nyelven programozhat\u00f3 RISC-V front-end, amely az adatfolyamok vez\u00e9rl\u00e9s\u00e9\u00e9rt felel\u0151s a f\u0151magokhoz, \u00e9s a 'teljes\u00edt\u00e9sig val\u00f3 v\u00e9grehajt\u00e1s' (run-to-completion) modellt alkalmazza a hagyom\u00e1nyos CPU-k funkcionalit\u00e1s\u00e1nak biztos\u00edt\u00e1sa \u00e9s a rendk\u00edv\u00fcl alacsony energiafogyaszt\u00e1s \u00e9rdek\u00e9ben.\n<li class=\"l\"> Vektori\u00e1lis SIMD-kieg\u00e9sz\u00edt\u0151, amely t\u00e1mogatja a RISC-V (RVV v1.0) vektoros utas\u00edt\u00e1sk\u00e9szlet kiterjeszt\u00e9seit, \u00e9s lehet\u0151v\u00e9 teszi t\u00f6bb oper\u00e1ci\u00f3 egyidej\u0171 v\u00e9grehajt\u00e1s\u00e1t nagy mennyis\u00e9g\u0171 adat felett.\n<li class=\"l\"> M\u00e1trix-kieg\u00e9sz\u00edt\u0151, amely hat\u00e9konyan v\u00e9grehajtja a kombin\u00e1lt szorz\u00e1s-\u00f6sszead\u00e1s (MAC) m\u0171veleteket, \u00e9s a neur\u00e1lis h\u00e1l\u00f3zatok alapvet\u0151 m\u0171veleteinek gyors\u00edt\u00e1s\u00e1ra lett tervezve.   <\/ul>\n<p>     <center><img decoding=\"async\" alt=\"A Google bemutatta a Coral NPU-t, egy ny\u00edlt platformot mesters\u00e9ges intelligencia gyors\u00edt\u00f3k l\u00e9trehoz\u00e1s\u00e1hoz. \" src=\"\/wp-content\/uploads\/2025\/10\/5a3195aa62210656469abd10237c3663.png\" style=\"display:block;margin: 0 auto;\" \/><\/center>    <\/p>\n<p>A fejleszt\u0151k sz\u00e1m\u00e1ra AI-modellekhez k\u00e9sz\u00fclt ford\u00edt\u00f3k\u00e9szlet (IREE \u00e9s TFLM), C nyelv\u0171 programk\u00e9sz\u00edt\u0151 \u00e9s szimul\u00e1tor \u00e1ll rendelkez\u00e9sre. T\u00e1mogatja a TensorFlow, JAX \u00e9s PyTorch keretrendszereken alapul\u00f3 AI-alkalmaz\u00e1sokban haszn\u00e1lt modellek ford\u00edt\u00e1s\u00e1t. A modell egy univerz\u00e1lis k\u00f6ztes reprezent\u00e1ci\u00f3ba ford\u00edt\u00f3dik, amelyet az LLVM seg\u00edts\u00e9g\u00e9vel alacsony szint\u0171 RISC-V utas\u00edt\u00e1sk\u00e9szlett\u00e9 alak\u00edtanak \u00e1t, amelyet a Coral NPU t\u00e1mogat.         <center><img decoding=\"async\" alt=\"A Google bemutatta a Coral NPU-t, egy ny\u00edlt platformot mesters\u00e9ges intelligencia gyors\u00edt\u00f3k l\u00e9trehoz\u00e1s\u00e1hoz. \" src=\"\/wp-content\/uploads\/2025\/10\/6401ed249ad2056b24a34bdb66b00b99.png\" style=\"display:block;margin: 0 auto;\" \/><\/center><br \/>\n<br \/>Forr\u00e1s: <a content=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/www.opennet.ru\/opennews\/art.shtml?num=64065\">opennet.ru<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>\u041a\u043e\u043c\u043f\u0430\u043d\u0438\u044f Google \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u0438\u043b\u0430 \u043e\u0442\u043a\u0440\u044b\u0442\u0443\u044e \u043f\u043b\u0430\u0442\u0444\u043e\u0440\u043c\u0443 Coral NPU (Neural Processing Unit), \u043f\u0440\u0435\u0434\u043b\u0430\u0433\u0430\u044e\u0449\u0443\u044e \u043e\u0442\u0440\u044b\u0442\u044b\u0439 \u0430\u043f\u043f\u0430\u0440\u0430\u0442\u043d\u044b\u0439 \u0443\u0441\u043a\u043e\u0440\u0438\u0442\u0435\u043b\u044c \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u0438 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u043d\u044b\u0439 \u0438\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u0430\u0440\u0438\u0439 \u0434\u043b\u044f \u0435\u0433\u043e \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u044f \u0441 \u0442\u0438\u043f\u043e\u0432\u044b\u043c\u0438 AI-\u0434\u0432\u0438\u0436\u043a\u0430\u043c\u0438. Coral \u043c\u043e\u0436\u043d\u043e \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0432 \u043a\u0430\u0447\u0435\u0441\u0442\u0432\u0435 \u043e\u0441\u043d\u043e\u0432\u044b \u0434\u043b\u044f \u0441\u043e\u0437\u0434\u0430\u043d\u0438\u044f \u044d\u043d\u0435\u0440\u0433\u043e\u044d\u0444\u0444\u0435\u043a\u0442\u0438\u0432\u043d\u044b\u0445 \u0441\u0438\u0441\u0442\u0435\u043c \u043d\u0430 \u043a\u0440\u0438\u0441\u0442\u0430\u043b\u043b\u0435 (SoC), \u043f\u0440\u0438\u0433\u043e\u0434\u043d\u044b\u0445 \u0434\u043b\u044f \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u044f \u0432 \u0441\u0438\u0441\u0442\u0435\u043c\u0430\u0445 \u0438\u043d\u0442\u0435\u0440\u043d\u0435\u0442\u0430 \u0432\u0435\u0449\u0435\u0439, Edge-\u0432\u044b\u0447\u0438\u0441\u043b\u0435\u043d\u0438\u0439 \u0438 \u043f\u043b\u0430\u0442\u0430\u0445 \u0441\u0431\u043e\u0440\u0430 \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u0438 \u0441 \u0434\u0430\u0442\u0447\u0438\u043a\u043e\u0432, \u0430 \u0442\u0430\u043a\u0436\u0435 [&hellip;]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":145377,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[702],"tags":[],"class_list":["post-145376","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.3 - aioseo.com -->\n\t<meta name=\"description\" content=\"\u041a\u043e\u043c\u043f\u0430\u043d\u0438\u044f Google \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u0438\u043b\u0430 \u043e\u0442\u043a\u0440\u044b\u0442\u0443\u044e \u043f\u043b\u0430\u0442\u0444\u043e\u0440\u043c\u0443 Coral NPU (Neural Processing Unit), \u043f\u0440\u0435\u0434\u043b\u0430\u0433\u0430\u044e\u0449\u0443\u044e \u043e\u0442\u0440\u044b\u0442\u044b\u0439 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