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Ȩ Ȩ > ¿¬±¸¹®Çå > ±¹³» ³í¹®Áö > Çѱ¹Á¤º¸°úÇÐȸ ³í¹®Áö > Á¤º¸°úÇÐȸ³í¹®Áö (Journal of KIISE)

Á¤º¸°úÇÐȸ³í¹®Áö (Journal of KIISE)

Current Result Document :

ÇѱÛÁ¦¸ñ(Korean Title) C++ ±â¹Ý ¹ü¿ë ¿ÀǼҽº µö·¯´× ÇÁ·¹ÀÓ¿öÅ© WICWIU
¿µ¹®Á¦¸ñ(English Title) C++ based General-purpose Open Source Deep Learning Framework, WICWIU
ÀúÀÚ(Author) ¹Úõ¸í   ±èÁö¿õ   ±âÀ±È£   ±èÁöÇö   À±¼º°á   ÃÖÀº¼­   ±èÀÎÁß   Chunmyong Park   Jeewoong Kim   Yunho Kee   Jihyeon Kim   Seonggyeol Yoon   Eunseo Choi   Injung Kim  
¿ø¹®¼ö·Ïó(Citation) VOL 46 NO. 03 PP. 0253 ~ 0259 (2019. 03)
Çѱ۳»¿ë
(Korean Abstract)
±¹³» ´ëÇÐÀ¸·Î´Â ÃÖÃÊ·Î °ø°³ÇÑ ¿ÀǼҽº µö·¯´× ÇÁ·¹ÀÓ¿öÅ© WICWIU¸¦ ¼Ò°³ÇÑ´Ù. WICWIU´Â ´Ù¾çÇÑ ¿¬»êÀÚ¿Í ¸ðµâ, ±×¸®°í ÀϹÝÀûÀÎ °è»ê ±×·¡ÇÁµéÀ» Ç¥ÇöÇÒ ¼ö ÀÖ´Â ½Å°æ¸Á ±¸Á¶¸¦ Á¦°øÇÏ¿© Inception, ResNet, DenseNet µî ³Î¸® »ç¿ëµÇ´Â ÃֽŠµö·¯´× ¸ðµ¨µéÀ» ±¸¼ºÇϱ⿡ ÃæºÐÇÑ ±â´ÉÀ» Á¦°øÇÑ´Ù. ¶ÇÇÑ, GPU ±â¹Ý ´ë±Ô¸ð º´·Ä ÄÄÇ»ÆÃÀ» Áö¿øÇØ ºü¸¥ ÇнÀÀÌ °¡´ÉÇÏ´Ù. ¸ðµç API°¡ C++·Î Á¦°øµÇ¾î C++ °³¹ßÀÚµéÀÌ ½±°Ô ÀûÀÀÇÒ ¼ö ÀÖÀ¸¸ç, C++ȯ°æ¿¡ ±â¹ÝÇϱ⠶§¹®¿¡ ÆÄÀ̽㠱â¹ÝÀÇ ÇÁ·¹ÀÓ¿öÅ©¿¡ ºñÇØ ¸Þ¸ð¸® ¹× ¼º´É ÃÖÀûÈ­¿¡µµ À¯¸®ÇÏ´Ù. µû¶ó¼­, ÇÁ·¹ÀÓ¿öÅ© ÀÚü¸¦ ÀÚ¿øÀÌ Á¦ÇÑµÈ È¯°æ¿¡ ¸Âµµ·Ï ¼öÁ¤Çϱ⿡µµ ¿ëÀÌÇÏ´Ù. ÀÏ°ü¼º ³ôÀº ÄÚµå¿Í API·Î ±¸¼ºµÇ¾î °¡µ¶¼º°ú È®À强ÀÌ ¿ì¼öÇϸç, Çѱ¹¾î ¹®¼­¸¦ Á¦°øÇØ ±¹³» °³¹ßÀÚµéÀÌ ½±°Ô Á¢±ÙÇÒ ¼ö ÀÖ´Ù. WICWIU´Â Apache 2.0 ¶óÀ̼±½º¸¦ Àû¿ëÇØ ¾î¶°ÇÑ ¿¬±¸ ¸ñÀû ¹× »ó¿ë ¸ñÀûÀ¸·Îµµ ÀÚÀ¯·Ó°Ô È°¿ëÇÒ ¼ö ÀÖ´Ù.
¿µ¹®³»¿ë
(English Abstract)
In this paper, we introduce WICWIU, the first open source deep learning framework among Korean universities. WICWIU provides a variety of operators and modules together with a network structure that can represent an arbitrary general computational graph. The WICWIU features are sufficient to compose widely used deep learning models such as Inception, ResNet, and DenseNet. WICWIU also supports GPU-based massive parallel computing which significantly accelerates the training of neural networks. It is also easily accessible for C developers because the whole API is provided in C . WICWIU has an advantage over Python-based frameworks in memory and performance optimization based on the C environment. This eases the customizability of WICWIU for environments with limited resources. WICWIU is readable and extensible because it is composed of C codes coupled with consistent APIs. With Korean documentation, it is particularly suitable for Korean developers. WICWIU applies the Apache 2.0 license which is available for any research or commercial purposes for free.
Å°¿öµå(Keyword) µö·¯´×   ½Å°æ¸Á   ÇÁ·¹ÀÓ¿öÅ©   ¿ÀǼҽº   WICWIU   deep learning   neural networks   framework   open source  
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