Optimizing Convolutional Neural Networks Utilizing Tensor Decomposition Techniques for Large-Scale Image Recognition Tasks

Abstract

Convolutional Neural Networks (CNNs) are integral to numerous applications in today's digital landscape. However, their computational demands often result in slow operation, especially when resources are constrained. This study introduces two tensor decomposition methods aimed at optimizing the performance of CNNs by minimizing the total number of operations and weights, while maintaining accuracy. The first method employs Canonical Polyadic (CP) decomposition to divide the convolutional layer into multiple rank 1 tensors. The second method uses Tucker decomposition to break down the convolutional layer into a compact core tensor and several matrices. The effectiveness of these methods was evaluated on widely-used convolutional architectures, VGG16 and LeNet, by substituting their convolutional layers with a series of decomposed layers, implemented using PyTorch and TensorLy. The CP decomposition method achieved a computational speed-up of 43% with a minimal accuracy reduction of less than 0.12%. Similarly, Tucker decomposition resulted in a 37% speed-up with an accuracy decrease of less than 0.16%. These findings suggest that the proposed tensor decomposition methods can significantly enhance the efficiency of CNNs without significantly impacting their performance.