HighLight: Efficient and Flexible DNN Acceleration with Hierarchical Structured Sparsity
Yannan Nellie Wu, Po-An Tsai, Saurav Muralidharan, Angshuman Parashar, Vivienne Sze, Joel Emer
Abstract
Due to complex interactions among various deep neural network (DNN) optimization techniques, modern DNNs can have weights and activations that are dense or sparse with diverse sparsity degrees. To offer a good trade-off between accuracy and hardware performance, an ideal DNN accelerator should have high flexibility to efficiently translate DNN sparsity into reductions in energy and/or latency without incurring significant complexity overhead.
Topics & Concepts
AccelerationComputer scienceDistributed computingArtificial intelligencePhysicsClassical mechanicsAdvanced Neural Network ApplicationsBrain Tumor Detection and ClassificationDomain Adaptation and Few-Shot Learning