11 Aug '17, 3pm

IBM Deep Learning Breaks Through

LAKE WALES, Fla. — IBM Research has reported an algorithmic breakthrough for deep learning that comes close to achieving the holy grail of ideal scaling efficiency: Its new distributed deep-learning (DDL) software enables a nearly linear speedup with each added processor (see figure). The development is intended to achieve similar speedups for each server added to IBM’s DDL algorithm. The aim “is to reduce the wait time associated with deep-learning training from days or hours to minutes or seconds,” according to IBM fellow and Think Blogger Hillery Hunter, director of the Accelerated Cognitive Infrastructure group at IBM Research. Hunter notes in a blog post on the development that “most popular deep-learning frameworks scale to multiple GPUs in a server, but not to multiple servers with GPUs.” The IBM team “wrote software and algorithms that automate and optimize the par...

Full article: http://www.eetimes.com/document.asp?doc_id=1332152&_mc=RS...

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This is a 904L stainless steel Rolex Oyster Sea Dweller Deep Sea, 116660. It has an integral 904L stainless steel flat thr...

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磁気の性質を使った論理演算素子、動作を確認 - EE Times Japan https://...

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All material on this site Copyright © 2005 - 2017 ITmedia Inc. All rights reserved. This site contains articles under lice...

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