# TIRAMISU: A Polyhedral Compiler for Dense and Sparse Deep Learning

arXiv: https://arxiv.org/abs/2005.04091 · HTML: https://mlsanity.com/paper/2005.04091/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2020.

## Built on by (most cited first, 7 of 7)

- [SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning](https://mlsanity.com/paper/2207.04606/) (2022)
- [A Deep Learning Based Cost Model for Automatic Code Optimization](https://mlsanity.com/paper/2104.04955/) (2021)
- [PIT: Optimization of Dynamic Sparse Deep Learning Models via Permutation Invariant Transformation](https://mlsanity.com/paper/2301.10936/) (2023)
- [LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers](https://mlsanity.com/paper/2403.11522/) (2024)
- [Agentic Auto-Scheduling: An Experimental Study of LLM-Guided Loop Optimization](https://mlsanity.com/paper/2511.00592/) (2025)
- [A Data-Centric Optimization Framework for Machine Learning](https://mlsanity.com/paper/2110.10802/) (2021)
- [A Reinforcement Learning Environment for Automatic Code Optimization in the MLIR Compiler](https://mlsanity.com/paper/2409.11068/) (2024)

## Abstract (arXiv)

In this paper, we demonstrate a compiler that can optimize sparse and recurrent neural networks, both of which are currently outside of the scope of existing neural network compilers (sparse neural networks here stand for networks that can be accelerated with sparse tensor algebra techniques). Our demonstration includes a mapping of sparse and recurrent neural networks to the polyhedral model along with an implementation of our approach in TIRAMISU, our state-of-the-art polyhedral compiler. We evaluate our approach on a set of deep learning benchmarks and compare our results with hand-optimized industrial libraries. Our results show that our approach at least matches Intel MKL-DNN and in some cases outperforms it by 5x (on multicore-CPUs).