HODEC: Towards Efficient High-Order DEcomposed Convolutional Neural Networks

Miao Yin

Abstract:

High-order decomposition is a widely used model compression approach towards compact convolutional neural
networks (CNNs). However, many of the existing solutions,
though can efficiently reduce CNN model sizes, are very difficult to bring considerable saving for computational costs,
especially when the compression ratio is not huge, thereby
causing the severe computation inefficiency problem. To
overcome this challenge, in this paper we propose efficient
High-Order DEcomposed Convolution (HODEC). By performing systematic explorations on the underlying reason
and mitigation strategy for the computation inefficiency, we
develop a new decomposition and computation-efficient execution scheme, enabling simultaneous reductions in computational and storage costs.
To demonstrate the effectiveness of HODEC, we perform empirical evaluations for various CNN models on different datasets. HODEC shows consistently outstanding
compression and acceleration performance. For compressing ResNet-56 on CIFAR-10 dataset, HODEC brings 67%
fewer parameters and 62% fewer FLOPs with 1.17% accuracy increase than the baseline model. For compressing ResNet-50 on ImageNet dataset, HODEC achieves 63%
FLOPs reduction with 0.31% accuracy increase than the
uncompressed model.