Ara2: Exploring Single- and Multi-Core Vector Processing with an Efficient RVV 1.0 Compliant Open-Source Processor

Abstract

Vector processing is highly effective in boosting processor performance and efficiency for data-parallel workloads. In this paper, we present Ara2, the first fully open-source vector processor to support the RISC-V V 1.0 frozen ISA. We evaluate Ara2's performance on a diverse set of data-parallel kernels for various problem sizes and vector-unit configurations, achieving an average functional-unit utilization of 95% on the most computationally intensive kernels. We pinpoint performance boosters and bottlenecks, including the scalar core, memories, and vector architecture, providing insights into the main vector architecture's performance drivers. Leveraging the openness of the design, we implement Ara2 in a 22nm technology, characterize its PPA metrics on various configurations (2-16 lanes), and analyze its microarchitecture and implementation bottlenecks. Ara2 achieves a state-of-the-art energy efficiency of 37.8 DP-GFLOPS/W (0.8V) and 1.35GHz of clock frequency (critical path: ~40 FO4 gates). Finally, we explore the performance and energy-efficiency trade-offs of multi-core vector processors: we find that multiple vector cores help overcome the scalar core issue-rate bound that limits short-vector performance. For example, a cluster of eight 2-lane Ara2 (16 FPUs) achieves more than 3x better performance than a 16-lane single-core Ara2 (16 FPUs) when executing a 32x32x32 matrix multiplication, with 1.5x improved energy efficiency.

To be published in: IEEE Transactions on Computers

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• A "New Ara" for Vector Computing: An Open Source Highly Efficient RISC-V V 1.0 Vector Processor Design
• A RISC-V Simulator and Benchmark Suite for Designing and Evaluating Vector Architectures
• Spatz: A Compact Vector Processing Unit for High-Performance and Energy-Efficient Shared-L1 Clusters
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Cited by in corpus (4)

• AraXL: A Physically Scalable, Ultra-Wide RISC-V Vector Processor Design for Fast and Efficient Computation on Long Vectors
• Zoozve: A Strip-Mining-Free RISC-V Vector Extension with Arbitrary Register Grouping Compilation Support (WIP)
• AraOS: Analyzing the Impact of Virtual Memory Management on Vector Unit Performance
• TROOP: At-the-Roofline Performance for Vector Processors on Low Operational Intensity Workloads