# FuseFlow: A Fusion-Centric Compilation Framework for Sparse Deep Learning on Streaming Dataflow

arXiv: https://arxiv.org/abs/2511.04768 · HTML: https://mlsanity.com/paper/2511.04768/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 35 of its 81 references that are in the map.
- First posted on arXiv 6 Nov 2025.

## Builds on (most cited first, 30 of 35)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [Big Bird: Transformers for Longer Sequences](https://mlsanity.com/paper/2007.14062/) (2020)
- [Revisiting Semi-Supervised Learning with Graph Embeddings](https://mlsanity.com/paper/1603.08861/) (2016)
- [ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases](https://mlsanity.com/paper/1705.02315/) (2017)
- [Learning Structured Sparsity in Deep Neural Networks](https://mlsanity.com/paper/1608.03665/) (2016)
- [Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks](https://mlsanity.com/paper/2102.00554/) (2021)
- [The State of Sparsity in Deep Neural Networks](https://mlsanity.com/paper/1902.09574/) (2019)
- [Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking](https://mlsanity.com/paper/1707.03815/) (2017)
- [Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge](https://mlsanity.com/paper/1612.08012/) (2016)
- [Sparse GPU Kernels for Deep Learning](https://mlsanity.com/paper/2006.10901/) (2020)
- [Cooperative Game Theory Approaches for Network Partitioning](https://mlsanity.com/paper/1707.03587/) (2017)
- [DNNFusion: Accelerating Deep Neural Networks Execution with Advanced Operator Fusion](https://mlsanity.com/paper/2108.13342/) (2021)
- [SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning](https://mlsanity.com/paper/2207.04606/) (2022)
- [Ramulator 2.0: A Modern, Modular, and Extensible DRAM Simulator](https://mlsanity.com/paper/2308.11030/) (2023)
- [FusedMM: A Unified SDDMM-SpMM Kernel for Graph Embedding and Graph Neural Networks](https://mlsanity.com/paper/2011.06391/) (2020)
- [Format Abstraction for Sparse Tensor Algebra Compilers](https://mlsanity.com/paper/1804.10112/) (2018)
- [Sparseloop: An Analytical Approach To Sparse Tensor Accelerator Modeling](https://mlsanity.com/paper/2205.05826/) (2022)
- [Compiler Support for Sparse Tensor Computations in MLIR](https://mlsanity.com/paper/2202.04305/) (2022)
- [The Sparse Abstract Machine](https://mlsanity.com/paper/2208.14610/) (2022)
- [Looplets: A Language For Structured Coiteration](https://mlsanity.com/paper/2209.05250/) (2022)
- [TeAAL: A Declarative Framework for Modeling Sparse Tensor Accelerators](https://mlsanity.com/paper/2304.07931/) (2023)
- [UniSparse: An Intermediate Language for General Sparse Format Customization](https://mlsanity.com/paper/2403.05802/) (2024)
- [Compilation of Modular and General Sparse Workspaces](https://mlsanity.com/paper/2404.04541/) (2024)
- [Stardust: Compiling Sparse Tensor Algebra to a Reconfigurable Dataflow Architecture](https://mlsanity.com/paper/2211.03251/) (2022)
- [SparseLNR: Accelerating Sparse Tensor Computations Using Loop Nest Restructuring](https://mlsanity.com/paper/2205.11622/) (2022)

## Abstract (arXiv)

As deep learning models scale, sparse computation and specialized dataflow hardware have emerged as powerful solutions to address efficiency. We propose FuseFlow, a compiler that converts sparse machine learning models written in PyTorch to fused sparse dataflow graphs for reconfigurable dataflow architectures (RDAs). FuseFlow is the first compiler to support general cross-expression fusion of sparse operations. In addition to fusion across kernels (expressions), FuseFlow also supports optimizations like parallelization, dataflow ordering, and sparsity blocking. It targets a cycle-accurate dataflow simulator for microarchitectural analysis of fusion strategies. We use FuseFlow for design-space exploration across four real-world machine learning applications with sparsity, showing that full fusion (entire cross-expression fusion across all computation in an end-to-end model) is not always optimal for sparse models-fusion granularity depends on the model itself. FuseFlow also provides a heuristic to identify and prune suboptimal configurations. Using Fuseflow, we achieve performance improvements, including a ~2.7x speedup over an unfused baseline for GPT-3 with BigBird block-sparse attention.