TeAAL: A Declarative Framework for Modeling Sparse Tensor Accelerators
Nandeeka Nayak, Toluwanimi O. Odemuyiwa, Shubham Ugare, Christopher W. Fletcher, Michael Pellauer, Joel Emer
Abstract
Over the past few years, the explosion in sparse tensor algebra workloads has led to a corresponding rise in domain-specific accelerators to service them. Due to the irregularity present in sparse tensors, these accelerators employ a wide variety of novel solutions to achieve good performance. At the same time, prior work on design-flexible sparse accelerator modeling does not express this full range of design features, making it difficult to understand the impact of each design choice and compare or extend the state-of-the-art.
Topics & Concepts
Variety (cybernetics)Computer scienceTensor (intrinsic definition)Domain (mathematical analysis)Range (aeronautics)Computational scienceTheoretical computer scienceParallel computingArtificial intelligenceMathematicsEngineeringAerospace engineeringMathematical analysisPure mathematicsParallel Computing and Optimization TechniquesTensor decomposition and applicationsComputational Physics and Python Applications