Fine-Grained Fusion: The Missing Piece in Area-Efficient State Space Model Acceleration

[2504.17333] Fine-Grained Fusion: The Missing Piece in Area-Efficient State Space Model Acceleration Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Hardware Architecture arXiv:2504.17333 (cs) [Submitted on 24 Apr 2025] Title: Fine-Grained Fusion: The Missing Piece in Area-Efficient State Space Model Acceleration Authors: Robin Geens , Arne Symons , Marian Verhelst View a PDF of the paper titled Fine-Grained Fusion: The Missing Piece in Area-Efficient State Space Model Acceleration, by Robin Geens and 2 other authors View PDF HTML (experimental) Abstract: State Space Models (SSMs) offer a promising alternative to transformers for long-sequence processing. However, their efficiency remains hindered by memory-bound operations, particularly in the prefill stage. While MARCA, a recent first effort to accelerate SSMs through a dedicated hardware accelerator, achieves great speedup over high-end GPUs, an analysis into the broader accelerator design space is lacking. This work systematically analyzes SSM acceleration opportunities both from the scheduling perspective through fine-grained operator fusion and the hardware perspective through design space exploration, using an extended version of the Stream modeling framework. Our results demonstrate that the improved data locality stemming from our optimized fusion and scheduling strategy enables a speedup of up to 4.8x over unfused execution, while our adaptive memory-aware fusion approach reduces on-chip memory requirements by an order of magnitude without sacrificing performance. We further explore accelerator design trade-offs, showing that a fusion-aware hardware architecture can achieve 1.78x higher performance than the state-of-the-art MARCA accelerator, within the same area budget. These results establish operator fusion as a key enabler for next-generation SSM accelerators. Subjects: Hardware Architecture (cs.AR) Cite as: arXiv:2504.17333 [cs.AR] (or arXiv:2504.17333v1 [cs.AR] for this version) https://doi.org/10.48550/arXiv.2504.17333 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1109/PACT65351.2025.00034 Focus to learn more DOI(s) linking to related resources Submission history From: Robin Geens [ view email ] [v1] Thu, 24 Apr 2025 07:52:02 UTC (821 KB) Full-text links: Access Paper: View a PDF of the paper titled Fine-Grained Fusion: The Missing Piece in Area-Efficient State Space Model Acceleration, by Robin Geens and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AR < prev | next > new | recent | 2025-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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