卷積神經網路是深度學習架構的一種,因為它對於圖形辨識有很好的效果,
所以目前研究界將焦點放在它身上, 但是它的訓練過程極為緩慢,即使是擁有
強大計算能力的GPU也需要幾天的時間才能訓練完成, 這樣便限制了它的應用
層面。
在這篇論文中,我們提出以可分離濾波器來提高卷積神經網路的速度, 首
先,在卷積神經網路中的二維濾波器用SVD來分解近似,並得到兩個一維的濾
波器, 其次,用這兩個一維濾波器來進行一維卷積並代替原本的二維卷積, 這
樣可以有效減少計算量。 在GPU實作中,我們實作了一個批次的SVD,它可以
同時處理很多的小矩陣SVD, 此外,我們提出三種不同的方法來計算卷積, 這
些方法會根據濾波器的大小來使用不同的記憶體,以便提高計算效率。
結果顯示,在前向傳導和後向傳導中,我們的可以得到1.38x ∼ 2.66x的加
速, 而以整體的訓練速度來看,我們得到13%的速度提升,但是準確度會下
降1%。

Convolutional neural networks are one of the most widely used deep architectures
in machine learning. While they achieve superior performance of recognition especially
for images, the training remains a computational challenge which prevents them from
practical uses. Even for GPUs possessing great computational power might take days
to produce results.
In this thesis, we propose a method based on separable filters to reduce the train-
ing time. First, by using SVDs, the 2D filters in the convolutional neural networks are
approximated by the product of two 1D filters. Second, two 1D convolutions are per-
formed with the previous 1D filters. In our GPU implementation, a batched SVDs that
can compute multiple small matrices simultaneously, and 3 methods which use different
memory spaces according to the filter size are presented.
Our experiment results shown that 1.38x ∼ 2.66x speedup was achieved in the for-
ward and the backward pass. The overall training time could be reduced by 13% with
1% drop in the recognition accuracy.

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