SparseTrain: Leveraging Dynamic Sparsity in Software for Training DNNs on General-Purpose SIMD Processors

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: ...

Convolutional Neural Networks (CNN) are a class of Ar- tificial Neural Networks (ANN) that are highly efficient at the pattern recognition tasks that underlie difficult AI prob- lems in a variety of domains, such as speech recognition, object ...

Convolutional Neural Networks (CNN) are a class of Ar- tificial Neural Networks (ANN) that are highly efficient at the pattern recognition tasks that underlie difficult AI prob- lems in a variety of domains, such as speech recognition, object ...