SEAM-V: A Hybrid-Decoupled RISC-V Vector Processor

Researchers have developed SEAM-V, a hybrid-decoupled vector execution architecture for RISC-V Vector Extension (RVV) that achieves a geometric-mean speedup of 1.34x across 17 representative kernels compared to an Ara-based tightly coupled RVV implementation. The architecture forms continuous streams of execute packets through task-level decoupling and VLIW-style packing, with speedups of 1.50x for one-dimensional variable-AVL kernels, 1.25x for BLAS and matrix kernels, and 1.27x for fixed-size applications, reaching nearly 3x at AVL=32.

Computer Science Hardware Architecture Submitted on 20 Jul 2026 Title:SEAM-V: A Hybrid-Decoupled RISC-V Vector Processor with Backend-Visible EP Context for Sustained Vector Throughput View PDF /pdf/2607.17899 HTML experimental https://arxiv.org/html/2607.17899v1 Abstract:Data-parallel workloads in deep learning and scientific computing continue to drive demand for higher processor throughput, energy efficiency, and scalability. The RISC-V Vector Extension RVV supports scalable execution through a vector-length-agnostic programming model. However, many tightly coupled implementations still rely on the scalar core to supply vector instructions one at a time, making execution susceptible to vector-instruction supply gaps, scalar-side progression delays, and conservative dependence handling in short-vector, loop-tail, and control/memory-interleaved phases. This paper presents SEAM-V, a hybrid-decoupled vector execution architecture for RVV. SEAM-V forms a continuous stream of execute packets EPs through task-level decoupling, local instruction supply, and VLIW-style packing. After an EP is serialized into individual requests, its EP identity and request-bound prefetch context remain visible to the dynamic vector backend, enabling same-EP candidate-hazard suppression and request-bound prefetching. The hybrid-dispatch path can also provide limited cross-EP vector overlap when the required safety conditions are satisfied. Cross-EP dependences, dependences not exempted by the EP contract, resource conflicts, and memory ordering remain dynamically managed by the backend. Cycle-accurate RTL evaluation shows that, compared with an Ara-based tightly coupled RVV implementation TC , SEAM-V achieves a geometric-mean speedup of 1.34x across 17 representative kernels. The one-dimensional variable-AVL, BLAS and matrix, and fixed-size application groups achieve speedups of 1.50x, 1.25x, and 1.27x, respectively. At AVL=32, the geometric-mean speedup across six one-dimensional vector kernels approaches 3x. References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender 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 https://info.arxiv.org/labs/index.html .