GPU를 이용한 신경망 구현
Implementation of Neural Networks using GPU
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초록
본 논문은 일반적인 그래픽스 하드웨어를 이용하여 더욱 빠른 신경망을 구현하고, 구현된 시스템을 영상 처리 분야에 적용함으로써 효용성을 검증한다. GPU의 병렬성을 효율적으로 사용하기 위하여, 다수의 입력벡터와 연결가중치벡터를 모아서 많은 내적연산을 하나의 행렬곱 연산으로 대체하였고, 시그모이드와 바이어스 항 덧셈 연산도 GPU 상에서 픽셀세이더로 구현하였다. ATI RADEON 9800 XT 보드를 이용하여 구현된 신경망 시스템은 CPU를 사용한 기존의 시스템과 비교하여 정확도의 차이 없이 30배 정도의 속도 향상을 얻을 수 있었다.
Abstract
We present a new use of common graphics hardware to perform a faster artificial neural network. And we examine the use of GPU enhances the time performance of the image processing system using neural network. In the case of parallel computation of multiple input sets, the vector-matrix products become matrix-matrix multiplications. As a result, we can fully utilize the parallelism of GPU. Sigmoid operation and bias term addition are also implemented using pixel shader on GPU. Our preliminary result shows a performance enhancement of about thirty times faster using ATI RADEON 9800 XT board.
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