Microarchitectural Co-Optimization for Sustained Throughput of RISC-V Multi-Lane Chaining Vector Processors

2026-04-24

AI Summary

This work addresses the performance limitations of RISC-V multi-lane chained vector processors, which struggle to approach theoretical peak performance due to bottlenecks in memory bandwidth, control dependencies, and operand forwarding. Building upon the open-source RVV processor Ara, the authors propose a microarchitectural co-optimization methodology tailored for multi-lane chained execution. By employing an ideal steady-state execution model to precisely identify critical bottlenecks, they introduce conservative dependency management enhancements, optimized memory transaction scheduling, and restructured result propagation pathways—all without increasing hardware resources. The resulting optimized system, Ara-Opt, achieves an average speedup of 1.33× under identical configurations and closes 12.2% of the performance gap relative to the roofline model on average. Notable improvements are observed in key kernels, with scal, axpy, ger, and gemm accelerating by 2.41×, 1.60×, 1.52×, and 1.42×, respectively.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Machine Learning: Hardware-aware ML
• Search and Optimization: Distributed Search

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Security and Privacy: Large-scale security measurements

Abstract

Modern RISC vector processors rely on the synergy of multi-lane parallelism and chaining to achieve high sustained throughput, yet their achieved performance often falls substantially short of the theoretical performance bound due to microarchitectural inefficiencies. In this work, we take the open-source RVV processor Ara as the target platform and analyze the sources of its sustained-throughput loss and optimize the design accordingly. We first establish an ideal multi-lane chaining execution model as a microarchitectural reference for the ideal steady-state progression of the vector backend. Based on this model, we attribute Ara's key bottlenecks to inefficiencies along three critical execution paths: memory-side inefficiencies in data supply and transaction issuance, control-side inefficiencies caused by conservative dependence management and issue control, and operand-delivery inefficiencies caused by access conflicts and result-propagation overhead. To address these bottlenecks, we propose a coordinated set of microarchitectural optimizations. Experimental results show that, without increasing raw memory bandwidth or changing the main processor configuration, Ara-Opt achieves a geometric-mean speedup of 1.33x over baseline Ara. Under roofline-based normalization, the geometric-mean gap-closed ratio reaches 12.2%. In particular, scal, axpy, ger, and gemm achieve speedups of approximately 2.41x, 1.60x, 1.52x, and 1.42x, with corresponding gap-closed ratios of 93.7%, 88.9%, 78.3%, and 59.3%, respectively. These results show that the proposed method can effectively recover sustained-throughput capability lost to microarchitectural inefficiencies in Ara under essentially unchanged hardware resource constraints, and move the implementation points of regular streaming and high-throughput workloads significantly closer to the theoretical performance bound.

Problem

• sustained throughput
• microarchitectural inefficiencies
• RISC-V vector processors
• multi-lane chaining
• performance gap

Innovation

• microarchitectural co-optimization
• multi-lane chaining
• sustained throughput
• RISC-V vector processor
• performance gap closing