TaiJiNet: Towards Partial Binarized Convolutional Neural Network for Embedded Systems

Abstract

We have witnessed the tremendous success of deep neural networks. However, this success comes with the considerable computation and storage costs which make it difficult to deploy these networks directly on resource-constrained embedded systems. To address this problem, we propose TaiJiNet, a binary-network-based framework that combines binary convolutions and pointwise convolutions, to reduce the computation and storage overhead while maintaining a comparable accuracy. Furthermore, in order to provide TaiJiNet with more flexibility, we introduce a strategy called partial binarized convolution to efficiently balance network performance and accuracy. We evaluate TaiJiNet with the CIFAR-10 and ImageNet datasets. The experimental results show that with the proposed TaiJiNet framework, the binary version of AlexNet can achieve 26x compression rate with a negligible 0.8% accuracy drop when compared with the full-precision AlexNet.