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arXiv · 2501.10301

AraXL: A Physically Scalable, Ultra-Wide RISC-V Vector Processor Design for Fast and Efficient Computation on Long Vectors

Navaneeth Kunhi Purayil·Matteo Perotti·Tim Fischer·Luca Benini
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Abstract

The ever-growing scale of data parallelism in today's HPC and ML applications presents a big challenge for computing architectures' energy efficiency and performance. Vector processors address the scale-up challenge by decoupling Vector Register File (VRF) and datapath widths, allowing the VRF to host long vectors and increase register-stored data reuse while reducing the relative cost of instruction fetch and decode. However, even the largest vector processor designs today struggle to scale to more than 8 vector lanes with double-precision Floating Point Units (FPUs) and 256 64-bit elements per vector register. This limitation is induced by difficulties in the physical implementation, which becomes wire-dominated and inefficient. In this work, we present AraXL, a modular and scalable 64-bit RISC-V V vector architecture targeting long-vector applications for HPC and ML. AraXL addresses the physical scalability challenges of state-of-the-art vector processors with a distributed and hierarchical interconnect, supporting up to 64 parallel vector lanes and reaching the maximum Vector Register File size of 64 Kibit/vreg permitted by the RISC-V V 1.0 ISA specification. Implemented in a 22-nm technology node, our 64-lane AraXL achieves a performance peak of 146 GFLOPs on computation-intensive HPC/ML kernels (>99% FPU utilization) and energy efficiency of 40.1 GFLOPs/W (1.15 GHz, TT, 0.8V), with only 3.8x the area of a 16-lane instance.

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Navaneeth Kunhi Purayil, Matteo Perotti, Tim Fischer, Luca Benini. 2025-01-17. AraXL: A Physically Scalable, Ultra-Wide RISC-V Vector Processor Design for Fast and Efficient Computation on Long Vectors. https://doi.org/10.23919/date64628.2025.10992880

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