{"id":34491,"date":"2019-10-31T21:58:39","date_gmt":"2019-10-31T18:58:39","guid":{"rendered":"https:\/\/prohoster.info\/blog\/intel-rabotaet-nad-opticheskimi-chipami-dlya-bolee-effektivnogo-ii\/"},"modified":"2019-10-31T21:58:39","modified_gmt":"2019-10-31T18:58:39","slug":"intel-rabotaet-nad-opticheskimi-chipami-dlya-bolee-effektivnogo-ii","status":"publish","type":"post","link":"https:\/\/prohoster.info\/et\/blog\/novosti-interneta\/intel-rabotaet-nad-opticheskimi-chipami-dlya-bolee-effektivnogo-ii","title":{"rendered":"Intel t\u00f6\u00f6tab optiliste kiipide kallal, et saavutada t\u00f5husam AI","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Fotonilised integreeritud seadmed v\u00f5i optilised kiibid v\u00f5ivad pakkuda mitmeid eeliseid v\u00f5rreldes elektrooniliste analoogidega, nagu energiatarbimise v\u00e4henemine ja viivituse v\u00e4hendamine arvutustes. Seet\u00f5ttu usuvad paljud teadlased, et need v\u00f5ivad olla \u00e4\u00e4rmiselt t\u00f5husad masin\u00f5ppe ja tehisintellekti (AI) \u00fclesannete t\u00e4itmisel. Ka Intel n\u00e4eb suurt potentsiaali r\u00e4ni fotonika rakendamiseks selles valdkonnas. Tema teadlaste r\u00fchma teadusartiklis<\/a><\/noindex> on \u00fcksikasjalikult kirjeldanud uusi meetodeid, mis v\u00f5ivad viia optiliste n\u00e4rviv\u00f5rkude valmimisele l\u00e4hemale.<\/p>\n

\"Intel<\/p>\n

Hiljutises Intel'i blogipostituses<\/a><\/noindex>, mille on p\u00fchendatud masin\u00f5ppele, k\u00e4sitletakse optiliste neuroniv\u00f5rkude uurimise algust. David A. B. Milleri ja Michael Recki teadust\u00f6\u00f6d n\u00e4itasid, et fotonketi t\u00fc\u00fcp, mida tuntakse kui Mach-Zehnder interferomeeter (MZI), saab konfigureerida 2 \u00d7 2 maatriksite korrutamiseks, samas kui MZI paigutamine kolmnurksesse v\u00f5rku suuremate maatriksite korrutamiseks v\u00f5ib luua skeemi, mis rakendab maatriksi ja vektori korrutamise algoritmi \u2014 peamine arvutus, mida kasutatakse masin\u00f5ppes.<\/p>\n

Uus Intel'i uurimus keskendub sellele, mis juhtub erinevate defektide ilmumise korral, millega optilised kiibid tootmise k\u00e4igus kokku puutuvad (kuna arvutifotonika on oma olemuselt analoogne), mist\u00f5ttu tekivad erinevused sama t\u00fc\u00fcpi kiipide arvutuste t\u00e4psuses. Kuigi sarnaseid uuringuid on juba varem l\u00e4bi viidud, on need keskendunud rohkem valmistamisej\u00e4rgsele optimeerimisele v\u00f5imalike ebat\u00e4psuste k\u00f5rvaldamiseks. Kuid see l\u00e4henemine ei ole h\u00e4sti skaleeritav, kuna v\u00f5rgud muutuvad j\u00e4rjest suuremaks, mis suurendab vajaliku arvutusv\u00f5imsuse mahtu optiliste v\u00f5rkude kohandamiseks. Selle asemel, et optimeerida p\u00e4rast valmistamist, vaatas Intel \u00fcle v\u00f5imaluse kiipide \u00fchekordseks treenimiseks enne valmistamist, kasutades h\u00e4irekindlat arhitektuuri. Murdmisena optiline n\u00e4rviv\u00f5rk treeniti \u00fcks kord, p\u00e4rast mida jagati treeningparameetrid mitme valmistatud v\u00f5rgu eksemplari vahel, mille komponentides esinevad erinevused.<\/p>\n

<\/center><\/p>\n

Inteli meeskond uuris kahte arhitektuuri, et luua MZI-p\u00f5hiseid tehisintellekti s\u00fcsteeme: GridNet ja FFTNet. GridNet paigutab MZI ennustatavalt v\u00f5rku, samas kui FFTNet paigutab need 'liblikate' kujul. P\u00e4rast nende koolitamist s\u00fcgava \u00f5ppimise standard\u00fclesande - k\u00e4sitsi kirjutatud numbrite (MNIST) - modelleerimiseks, leidsid teadlased, et GridNet saavutas k\u00f5rgema t\u00e4psuse kui FFTNet (98% vs 95%), kuid FFTNet'i arhitektuur osutus 'm\u00e4rkimisv\u00e4\u00e4rselt usaldusv\u00e4\u00e4rsemaks'. Tegelikult langes GridNet'i tulemus alla 50%, kui lisati kunstlik m\u00fcra (h\u00e4ired, mis imiteerivad v\u00f5imalikke defekte optiliste kiipide tootmisel), samas kui FFTNet'i tulemus j\u00e4i peaaegu muutumatuks.<\/p>\n

Teadlased v\u00e4idavad, et nende uuringud loobid aluse tehisintellekti \u00f5ppemeetoditele, mis aitavad v\u00e4ltida optiliste kiipide peenh\u00e4\u00e4lestamise vajadust p\u00e4rast tootmist, s\u00e4\u00e4stes v\u00e4\u00e4rtuslikku aega ja ressursse.<\/p>\n

'Nagu igas tootmisprotsessis, tekivad teatud defektid, mis t\u00e4hendavad, et kiipide vahel on v\u00e4iksed erinevused, ja need m\u00f5jutavad arvutuste t\u00e4psust,' kirjutab Inteli AI tootegrupi vanemdirektor Casimir Wierzynski. 'Kui optilised neuroniv\u00f5rgud saavad tehisintellekti riistvara \u00f6kos\u00fcsteemi eluj\u00f5uliseks osaks, peavad nad liikuma suuremate kiipide ja t\u00f6\u00f6stustootmise tehnoloogiate suunas. Meie uuringud n\u00e4itavad, et \u00f5ige arhitektuuri eelvalik v\u00f5ib m\u00e4rkimisv\u00e4\u00e4rselt suurendada t\u00f5en\u00e4osust, et saadud kiibid saavutavad soovitud j\u00f5udluse, isegi tootmisvariatsioonide olemasolul.'<\/p>\n

Samas, kui Intel keskendub peamiselt teaduslikule uurimist\u00f6\u00f6le, asutas Massachusettsi Tehnoloogiainstituudi kandidaat f\u00fc\u00fcsika ja matemaatika doktorikraadiga Yichen Shen Bostoni startup'i Lightelligence, mis on t\u00f5mmanud 10,7 miljoni USA dollari suuruse riskiinvesteeringu ja hiljuti demonstreeris<\/a><\/noindex> optikachippide protot\u00fc\u00fcp masin\u00f5ppe jaoks, mis on 100 korda kiirem kui t\u00e4nap\u00e4evased elektroonilised chippid ning v\u00e4hendab oluliselt energiatarbimist, mis veel kord t\u00f5endab fotoniliste tehnoloogiate perspektiive.<\/p>\n

<\/p>\n

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Allikas: 3dnews.ru<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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Seet\u00f5ttu arvavad paljud teadlased, et need v\u00f5ivad osutuda \u00e4\u00e4rmiselt t\u00f5husaks masin\u00f5ppe ja tehisintellekti (AI) \u00fclesannetes. Ka Intel n\u00e4eb suurt potentsiaali r\u00e4ni fotonika rakendamiseks","canonical_url":"https:\/\/prohoster.info\/et\/blog\/novosti-interneta\/intel-rabotaet-nad-opticheskimi-chipami-dlya-bolee-effektivnogo-ii","robots":"max-image-preview:large","keywords":"","webmasterTools":{"miscellaneous":""},"schema":null,"og:locale":"et_EE","og:site_name":"ProHoster | \u041a\u0443\u043f\u0438\u0442\u044c \u043d\u0430\u0434\u0435\u0436\u043d\u044b\u0439 \u0445\u043e\u0441\u0442\u0438\u043d\u0433 \u0434\u043b\u044f \u0441\u0430\u0439\u0442\u043e\u0432 \u0441 \u0437\u0430\u0449\u0438\u0442\u043e\u0439 \u043e\u0442 DDoS, VPS VDS \u0441\u0435\u0440\u0432\u0435\u0440\u044b","og:type":"article","og:title":"\ud83e\udd47Intel \u0440\u0430\u0431\u043e\u0442\u0430\u0435\u0442 \u043d\u0430\u0434 \u043e\u043f\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u043c\u0438 \u0447\u0438\u043f\u0430\u043c\u0438 \u0434\u043b\u044f \u0431\u043e\u043b\u0435\u0435 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