SparseTrain: Exploiting Dataflow Sparsity for Efficient Convolutional Neural Networks Training
Pengcheng Dai, Jianlei Yang, Xucheng Ye, Xingzhou Cheng, Junyu Luo, Linghao Song, Yiran Chen, Wang Kang
Abstract
Training Convolutional Neural Networks (CNNs) usually requires a large number of computational resources. In this paper, SparseTrain is proposed to accelerate CNN training by fully exploiting the sparsity. It mainly involves three levels of innovations: activation gradients pruning algorithm, sparse training dataflow, and accelerator architecture. By applying a stochastic pruning algorithm on each layer, the sparsity of back-propagation gradients can be increased dramatically without degrading training accuracy and convergence rate. Moreover, to utilize both natural sparsity (resulted from ReLU or Pooling layers) and artificial sparsity (brought by pruning algorithm), a sparse-aware architecture is proposed for training acceleration. This architecture supports forward and back-propagation of CNN by adopting 1-Dimensional convolution dataflow. We have built a cycle-accurate architecture simulator to evaluate the performance and efficiency based on the synthesized design with 14nm FinFET technologies. Evaluation results on AlexNet/ResNet show that SparseTrain could achieve about 2.7× speedup and 2.2× energy efficiency improvement on average compared with the original training process.