We present the iterative two-pass decomposition flow to accelerate existing convolutional neural networks (CNNs).   The proposed rank selection algorithm can effectively determine the proper ranks of the target convolutional layers for the low rank approximation. Our two-pass CP-decomposition helps prevent from the instability problem. The iterative flow makes the decomposition of the deeper networks systematic. The experiment results shows that VGG16 can be accelerated with a 6.2x measured speedup while the accuracy drop remains only 1.2%.

We present the iterative two-pass CP decomposition flow to effectively accelerate existing convolutional neural networks (CNNs). The paper proposes a novel workflow for acceleration and compression of CNNs and also proposes a way to determine the target rank of each layer given the target overall acceleration.  This paper addresses the problem of learning a low rank tensor filter operation for filtering layers in deep neural networks (DNNs). 