{"id":30179,"date":"2019-10-31T21:34:00","date_gmt":"2019-10-31T18:34:00","guid":{"rendered":"https:\/\/prohoster.info\/blog\/asic-dlya-mashinnogo-obucheniya-dolzhny-proektirovatsya-avtomaticheski\/"},"modified":"2019-10-31T21:34:00","modified_gmt":"2019-10-31T18:34:00","slug":"asic-dlya-mashinnogo-obucheniya-dolzhny-proektirovatsya-avtomaticheski","status":"publish","type":"post","link":"https:\/\/prohoster.info\/sv\/blog\/asic-dlya-mashinnogo-obucheniya-dolzhny-proektirovatsya-avtomaticheski","title":{"rendered":"ASIC f\u00f6r maskininl\u00e4rning b\u00f6r designas automatiskt","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Det r\u00e5der knappast n\u00e5gra tvivel om att design av skr\u00e4ddarsydda ASIC:er \u00e4r en l\u00e5ngt ifr\u00e5n enkel och snabb process. Och det \u00e4r just det man \u00f6nskar och beh\u00f6ver \u2013 att det ska g\u00e5 snabbare: idag f\u00e5r man algoritmen, och om en vecka h\u00e4mtar man den f\u00e4rdiga digitala designen. Problemet \u00e4r att super-specialiserade ASIC:er \u00e4r i stort sett en hantverksprodukt. De efterfr\u00e5gas s\u00e4llan i miljonpartier, som det skulle vara m\u00f6jligt att spendera hur mycket pengar och personalresurser som helst p\u00e5 att utveckla om det beh\u00f6vs p\u00e5 kort tid. Specialiserade, och d\u00e4rmed mest effektiva, ASIC:er f\u00f6r att l\u00f6sa sina uppgifter m\u00e5ste utvecklas billigare, vilket \u00e4r oerh\u00f6rt aktuellt i den nuvarande utvecklingen av maskininl\u00e4rning. P\u00e5 denna front g\u00e5r det inte l\u00e4ngre att f\u00f6rlita sig p\u00e5 den kunskap som har samlats av datormarknaden och s\u00e4rskilt de framsteg som gjorts inom GPU-teknologin f\u00f6r maskininl\u00e4rning (ML).<\/p>\n

\"ASIC<\/p>\n

F\u00f6r att p\u00e5skynda designprocessen f\u00f6r ASIC f\u00f6r ML-uppgifter inr\u00e4ttar DARPA en ny program \u2013 Real Time Machine Learning (RTML). Programmet f\u00f6r verklig tidens maskininl\u00e4rning syftar till att utveckla en kompilator eller programplattform som kan automatiskt designa chiparkitektur f\u00f6r specifika ML-ramverk. Plattformen ska automatiskt analysera den f\u00f6reslagna algoritmen f\u00f6r maskininl\u00e4rning och datam\u00e4ngden f\u00f6r att tr\u00e4na denna algoritm, varefter den ska generera kod p\u00e5 Verilog f\u00f6r att skapa en specialiserad ASIC. Speciella inom algoritmutveckling f\u00f6r ML har inte kunskaper inom chipdesign, och chipdesigners \u00e4r s\u00e4llan bekanta med principerna f\u00f6r maskininl\u00e4rning. Programmet RTML b\u00f6r underl\u00e4tta s\u00e5 att styrkorna hos b\u00e5da parter f\u00f6renas i en automatiserad plattform f\u00f6r utveckling av ASIC f\u00f6r maskininl\u00e4rning.<\/p>\n

Under program RTML's lifecycle, the solutions found will need to be validated in two main application areas: operating in 5G networks and image processing. The RTML program and the software platforms created for the automated design of ML accelerators will also be used for the development and testing of new ML algorithms and datasets. Therefore, even before the design of the \"silicon,\" the potential of new frameworks can be assessed. The partner of DARPA in the RTML program will be the National Science Foundation (NSF), which is also engaged in machine learning issues and the development of ML algorithms. The developed compiler will be handed over to NSF, and in return, DARPA expects to receive a compiler and a platform for designing ML algorithms. Furthermore, hardware design and the creation of algorithms will become a comprehensive solution, leading to the emergence of real-time self-learning machine systems.<\/p>\n

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K\u00e4lla: 3dnews.ru<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

\u0412\u0440\u044f\u0434 \u043b\u0438 \u043a\u0442\u043e-\u0442\u043e \u0431\u0443\u0434\u0435\u0442 \u0441\u043f\u043e\u0440\u0438\u0442\u044c \u0441 \u0442\u0435\u043c, \u0447\u0442\u043e \u043f\u0440\u043e\u0435\u043a\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u0437\u0430\u043a\u0430\u0437\u043d\u044b\u0445 \u0411\u0418\u0421 (ASIC) \u2015 \u044d\u0442\u043e \u0434\u0430\u043b\u0435\u043a\u043e \u043d\u0435 \u043f\u0440\u043e\u0441\u0442\u043e\u0439 \u0438 \u043d\u0435 \u0431\u044b\u0441\u0442\u0440\u044b\u0439 \u043f\u0440\u043e\u0446\u0435\u0441\u0441. \u0410 \u0432\u0435\u0434\u044c \u0445\u043e\u0447\u0435\u0442\u0441\u044f \u0438 \u043d\u0443\u0436\u043d\u043e, \u0447\u0442\u043e\u0431\u044b \u0431\u044b\u043b\u043e \u0431\u044b\u0441\u0442\u0440\u0435\u0435: \u0441\u0435\u0433\u043e\u0434\u043d\u044f \u0432\u044b\u0434\u0430\u043b \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c, \u0430 \u0447\u0435\u0440\u0435\u0437 \u043d\u0435\u0434\u0435\u043b\u044e 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