# TeAAL: A Declarative Framework for Modeling Sparse Tensor Accelerators

arXiv: https://arxiv.org/abs/2304.07931 · HTML: https://mlsanity.com/paper/2304.07931/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2023.

## Built on by (most cited first, 4 of 4)

- [SystolicAttention: Fusing FlashAttention within a Single Systolic Array](https://mlsanity.com/paper/2507.11331/) (2025)
- [PLUM: Improving Inference Efficiency By Leveraging Repetition-Sparsity Trade-Off](https://mlsanity.com/paper/2312.01581/) (2023)
- [FuseFlow: A Fusion-Centric Compilation Framework for Sparse Deep Learning on Streaming Dataflow](https://mlsanity.com/paper/2511.04768/) (2025)
- [Metabolic cost of information processing in Poisson variational autoencoders](https://mlsanity.com/paper/2602.13421/) (2026)

## Abstract (arXiv)

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. To address this, we propose TeAAL: a language and simulator generator for the concise and precise specification and evaluation of sparse tensor algebra accelerators. We use TeAAL to represent and evaluate four disparate state-of-the-art accelerators -- ExTensor, Gamma, OuterSPACE, and SIGMA -- and verify that it reproduces their performance with high accuracy. Finally, we demonstrate the potential of TeAAL as a tool for designing new accelerators by showing how it can be used to speed up vertex-centric programming accelerators -- achieving 1.9× on BFS and 1.2× on SSSP over GraphDynS.