A "New Ara" for Vector Computing: An Open Source Highly Efficient RISC-V V 1.0 Vector Processor Design
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Abstract (arXiv)
Vector architectures are gaining traction for highly efficient processing of data-parallel workloads, driven by all major ISAs (RISC-V, Arm, Intel), and boosted by landmark chips, like the Arm SVE-based Fujitsu A64FX, powering the TOP500 leader Fugaku. The RISC-V V extension has recently reached 1.0-Frozen status. Here, we present its first open-source implementation, discuss the new specification's impact on the micro-architecture of a lane-based design, and provide insights on performance-oriented design of coupled scalar-vector processors. Our system achieves comparable/better PPA than state-of-the-art vector engines that implement older RVV versions: 15% better area, 6% improved throughput, and FPU utilization >98.5% on crucial kernels.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Oct 2022.
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Citing papers per year: 2023: 3; 2024: 1; 2025: 1.
5 papers.
- 2023A Survey on Deep Learning Hardware Accelerators for Heterogeneous HPC Platformscited by 21
- 2023Quark: An Integer RISC-V Vector Processor for Sub-Byte Quantized DNN Inferencecited by 2
- 2024SPEED: A Scalable RISC-V Vector Processor Enabling Efficient Multi-Precision DNN Inferencecited by 2
- 2023Sparq: A Custom RISC-V Vector Processor for Efficient Sub-Byte Quantized Inferencecited by 1
- 2025Tensor Program Optimization for the RISC-V Vector Extension Using Probabilistic Programscited by 1
- 2025Tensor Program Optimization for the RISC-V Vector Extension Using Probabilistic Programs2 Jul 2025
- 2024SPEED: A Scalable RISC-V Vector Processor Enabling Efficient Multi-Precision DNN Inference21 Sep 2024
- 2023A Survey on Deep Learning Hardware Accelerators for Heterogeneous HPC Platforms27 Jun 2023
- 2023Sparq: A Custom RISC-V Vector Processor for Efficient Sub-Byte Quantized Inference16 Jun 2023
- 2023Quark: An Integer RISC-V Vector Processor for Sub-Byte Quantized DNN Inference12 Feb 2023