January 2017
Beginner to intermediate
312 pages
7h 3m
Korean
Content preview from 밑바닥부터 시작하는 딥러닝
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274
밑바닥부터 시작하는 딥러닝
8.3.1
풀어야 할 숙제
딥러닝의 고속화 얘기를 시작하기 앞서, 딥러닝에서는 어떠한 처리에 시간이 소요되는지를 보
겠습니다. [그림
8
-
14
]는
AlexNet
의
forward
처리(순전파)에서 각 층이 소비하는 시간을
원 그래프로 보여줍니다.
그림
8-14
AlexNet
의
forward
처리 시 각 층의 시간 비율 : 왼쪽이
GPU
, 오른쪽이
CPU
를 사용한 경우. ‘
conv
’는
합성곱 계층, ‘
pool
’은 풀링 계층, ‘
fc
’는 완전연결 계층, ‘
norm
’은 정규화 계층이다.
[
26
]
그림에서 보듯
AlexNet
에서는 오랜 시간을 합성곱 계층에서 소요합니다. 실제로 합성곱 계층
의 처리 시간을 다 더하면
GPU
에서는 전체의
95
%,
CPU
에서는 전체의
89
%까지 달하지요!
그래서 합성곱 계층에서 이뤄지는 연산을 어떻게 고속으로 효율적으로 하느냐가 딥러닝의 과
제입니다. [그림
8
-
14
]는 추론 때의 결과지만, 학습 시에도 마찬가지로 합성곱 계층에서 많은
시간을 소비하게 됩니다.
합성곱 계층에서 수행하는 연산은 “
7
.
2
합성곱 계층”에서 설명했듯이, 결국 ‘단일 곱셈-누산’입니
다. 그래서 딥러닝 고속화라는 주제는 대량의 ‘단일 곱셈-누산’을 어떻게 고속으로 효율적으로 계산하느냐는
것입니다.
8.3.2
GPU
를 활용한 고속화 ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.