New Compiler FuseFlow Enables Cross-Expression Fusion for Sparse Machine Learning Models

Global: New Compiler FuseFlow Enables Cross-Expression Fusion for Sparse Machine Learning Models

A team of researchers announced a new compiler named FuseFlow in a paper posted to arXiv, aiming to improve the efficiency of sparse deep‑learning models on reconfigurable dataflow architectures. The work targets the growing need for specialized hardware and sparse computation as machine‑learning models continue to scale, and it offers a method to translate PyTorch models into fused sparse dataflow graphs.

Background and Motivation

As deep‑learning models increase in size, conventional dense computation becomes increasingly power‑hungry, prompting interest in sparse computation and hardware accelerators. Existing toolchains often handle sparse operations in isolation, limiting the potential gains from cross‑kernel optimization.

FuseFlow Architecture and Capabilities

FuseFlow is presented as the first compiler that supports general cross‑expression fusion of sparse operations. It converts PyTorch‑defined models into fused dataflow graphs, applying optimizations such as parallelization, dataflow ordering, and sparsity blocking. The compiler targets a cycle‑accurate dataflow simulator, enabling microarchitectural analysis of various fusion strategies.

Design Space Exploration Findings

The authors used FuseFlow to explore design‑space trade‑offs across four real‑world machine‑learning applications that incorporate sparsity. Their results indicate that full fusion—combining all computation in an end‑to‑end model—is not universally optimal; the ideal granularity depends on the specific model characteristics.

Performance Gains Demonstrated

In a case study involving GPT‑3 with BigBird block‑sparse attention, FuseFlow achieved approximately a 2.7× speedup compared with an unfused baseline. The compiler also includes a heuristic to identify and prune suboptimal configurations, further enhancing performance.

Implications for Future Hardware Design

By providing a systematic way to evaluate fused sparse dataflows, FuseFlow could inform the design of next‑generation reconfigurable dataflow architectures, potentially leading to more energy‑efficient AI accelerators.

Limitations and Future Work

The study relies on simulation rather than physical hardware, and the authors acknowledge that real‑world implementation may reveal additional challenges. Future research is expected to validate the compiler’s effectiveness on physical RDAs and to extend support to a broader range of sparse patterns.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.