{"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\/news\/intel-rabotaet-nad-opticheskimi-chipami-dlya-bolee-effektivnogo-ii","title":{"rendered":"Intel t\u00f6\u00f6tab optiliste kiipide kallal, et t\u00e4iustada tehisintellekti t\u00f5husust","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Fotonintegratsiooniskeemid v\u00f5i optilised kiibid omavad t\u00f5en\u00e4oliselt mitmeid eeliseid v\u00f5rreldes elektrooniliste analoogidega, nagu energiatarbimise v\u00e4henemine ja arvutuste viivituse v\u00e4hendamine. Just seet\u00f5ttu peavad paljud teadlased neid masin\u00f5ppe ja tehisintellekti (AI) \u00fclesannete t\u00e4itmiseks \u00e4\u00e4rmiselt t\u00f5husaks. Ettev\u00f5te Intel n\u00e4eb samuti suurte v\u00f5imaluste avardumist r\u00e4ni fotonika rakendamisel selles suunas. Tema teadlaste r\u00fchm teadusartiklis<\/a><\/noindex> kirjeldas p\u00f5hjalikult uusi meetodeid, mis viivad optilisi n\u00e4rviv\u00f5rke \u00fche sammu l\u00e4hemale reaalsusele.<\/p>\n

\"Intel<\/p>\n

Viimases Inteli blogi postituses<\/a><\/noindex>, mis on p\u00fchendatud masin\u00f5ppele, k\u00e4sitletakse, kuidas algasid uuringud optiliste n\u00e4rviv\u00f5rkude alal. David A. B. Milleri ja Michael Recki teadust\u00f6\u00f6d n\u00e4itasid, et fotondiidi t\u00fc\u00fcp, mida tuntakse Mach-Zehnder interferomeetrina (MZI), saab seadistada 2 \u00d7 2 suuruste maatriksite korrutamiseks, ja kui paigutada MZI kolmnurkse v\u00f5rgu sisse suuremate maatriksite korrutamiseks, on v\u00f5imalik saavutada skeem, mis rakendab maatriksi ja vektori korrutamise algoritmi \u2013 peamine arvutus, mida kasutatakse masin\u00f5ppes.<\/p>\n

Uus Intel'i uurimus keskendub sellele, mis juhtub erinevate defektide ilmnemisel, millega optilised kiibid tootmise k\u00e4igus kokku puutuvad (kuna arvutuslik fotoonika on oma olemuselt analoogne), tekitades erinevusi sama t\u00fc\u00fcpi kiipide vahel arvutuste t\u00e4psuses. Kuigi selliseid uuringuid on varem l\u00e4bi viidud, on need keskendunud enamasti tootmisj\u00e4rgsete optimeerimiste tegemisele v\u00f5imalike ebat\u00e4psuste k\u00f5rvaldamiseks. Kuid see l\u00e4henemine on halva skaleeritavusega, kuna v\u00f5rgud kasvavad j\u00e4rjest suuremaks, mis toob endaga kaasa vajaduse suurema arvutusv\u00f5imsuse j\u00e4rele optiliste v\u00f5rkude h\u00e4\u00e4lestamiseks. Intel ei keskendunud tootmisj\u00e4rgsele optimeerimisele, vaid kaalus v\u00f5imalust koolitada kiipe \u00fchekordselt enne tootmist, kasutades t\u00f5rke suhtes t\u00f5husat arhitektuuri. Standardne optiline n\u00e4rviv\u00f5rk koolitati \u00fchekordselt, mille j\u00e4rel koolituse parameetrid jaotati mitme valmistatud v\u00f5rgu eksemplari vahel, mille komponentides on erinevusi.<\/p>\n

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

Intel'i meeskond uuris kahte arhitektuuri tehisintellekti s\u00fcsteemide rajamiseks MZI alusel: GridNet ja FFTNet. GridNet paigutab MZI v\u00f5rku ettearvatavalt, samas kui FFTNet paigutab need nagu \u201eliblikad\u201c. P\u00e4rast m\u00f5lema koolitamist s\u00fcgava \u00f5ppe tsentraliseeritud \u00fclesande mudelil, mis k\u00e4sitleb k\u00e4ekirja numbrite (MNIST) tuvastamist, leidsid teadlased, et GridNet saavutas k\u00f5rgema t\u00e4psuse kui FFTNet (98% vs 95%), kuid FFTNet arhitektuur osutus \u201eoluliselt usaldusv\u00e4\u00e4rsemaks\u201c. Tegelikult langes GridNet'i j\u00f5udlus madalamale kui 50% kunstliku m\u00fcra lisamisega (h\u00e4ired, mis simuleerivad v\u00f5imalikke defekte optiliste kiipide tootmisel), samas kui FFTNet'i j\u00f5udlus j\u00e4i pea konstantseks.<\/p>\n

Teadlased v\u00e4idavad, et nende uuringud loovad aluse tehisintellekti \u00f5ppe meetoditele, mis aitavad v\u00e4ltida vajadust optiliste kiipide t\u00e4iendava h\u00e4\u00e4lestamise j\u00e4rele p\u00e4rast tootmist, s\u00e4\u00e4stes v\u00e4\u00e4rtuslikku aega ja ressursse.<\/p>\n

\u201eNagu igas tootmisprotsessis, esinevad teatud defektid, mis t\u00e4hendab, et kiipide vahel v\u00f5ivad olla v\u00e4ikesed erinevused, ja need m\u00f5jutavad arvutuste t\u00e4psust,\u201c kirjutab Intel AI tootegrupi vanemdirektor Kazimir Wierzynski. \u201eKui optilised n\u00e4rviv\u00f5rgud peaksid saama eluj\u00f5uliseks osaks tehisintellekti riistvara \u00f6kos\u00fcsteemist, peaksid nad liikuma suuremate kiipide ja t\u00f6\u00f6stuslikuma tootmisprotsessi suunas. Meie uuringud n\u00e4itavad, et \u00f5ige arhitektuuri eelnevalt valimine v\u00f5ib oluliselt suurendada t\u00f5en\u00e4osust, et saadud kiibid saavutavad soovitud j\u00f5udluse isegi tootmisvariatsioonide olemasolu korral.\u201c<\/p>\n

Samal ajal, kui Intel peamiselt uurimist\u00f6\u00f6d teeb, asutas Massachusettsi Tehnoloogia Instituudi f\u00fc\u00fcsikateaduste kandidaat Yichen Shen Bostonis asuva idufirma Lightelligence, mis on saanud riskikapitali investeeringu summas 10,7 miljonit dollarit ja hiljuti demonstreeris<\/a><\/noindex> masin\u00f5ppe optilise kiibi protot\u00fc\u00fcbi, mis on 100 korda kiiremad kui t\u00e4nap\u00e4eva elektroonilised kiibid ning v\u00e4hendab samal ajal energiatarbimist, n\u00e4idates seega uuesti fotonite tehnoloogiate perspektiive.<\/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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