SparTA - Deep-Learning Model Sparsity via Tensor-with-Sparsity-Attribute

This course aims to teach learners about deep-learning model sparsity through a new abstraction called Tensor-with-Sparsity-Attribute (TeSA). The goal is to enable the specification, propagation, and utilization of sparsity attributes and patterns across the entire deep learning model to create highly efficient operators. By using the SparTA framework, learners will be able to achieve significant speedups on inference latency compared to existing sparse solutions. The course covers topics such as computation capacity vs. DNN model size, evolving sparsity patterns, SparTA system architecture, execution transformation, code specialization, and evaluation on various patterns and models. The intended audience for this course includes individuals interested in deep learning, model optimization, and efficiency in neural networks.