# SEAM-V: A Hybrid-Decoupled RISC-V Vector Processor > Source: > Published: 2026-07-22 01:03:44+00:00 # 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).