SEAM-V: A Hybrid-Decoupled RISC-V Vector Processor with Backend-Visible Packet Semantics and Source-Lifetime-Aware Scheduling

Abstract

Data-parallel workloads in deep learning and scientific computing continue to increase the demands on processor throughput, energy efficiency, and scalability. The RISC-V Vector Extension (RVV) supports scalable execution through a vector-length-agnostic model, yet many tightly coupled implementations still rely on the scalar core to supply vector instructions individually and are therefore constrained by instruction supply, scalar-side progress, memory stalls, and conservative dependence management in short-vector, loop-tail, and control/memory-interleaved scenarios. This paper presents SEAM-V, a hybrid-decoupled RVV processor that uses task-level decoupling, local instruction supply, and VLIW-style packing to form a continuous stream of execute packets (EPs). During EP formation and request serialization, SEAM-V preserves the association between prefetch intent and the corresponding load to support request-bound prefetching, while lane-level source-read completion is used to release pure write-after-read (WAR) dependences early; other dependences remain governed by conventional mechanisms. Relative to an Ara-based tightly coupled baseline (TC), SEAM-V achieves a geometric mean speedup of 1.38x across 17 representative kernel configurations. The one-dimensional vector, BLAS and matrix, and fixed-size application workload groups achieve 1.56x, 1.35x, and 1.23x, respectively. Synthesis and power analysis show that SEAM-V increases total cell area by 4.29% and geometric mean runtime power by 17.30%, while reducing task energy by 12.70%, demonstrating improved sustained execution efficiency and task-level energy efficiency with limited area overhead.