发表机构
National Institute of Science Education and Research; Sri Sri University; Gandhi Institute of Engineering and Technology University; Homi Bhabha National Institute（国家科学教育与研究所; 斯里斯里大学; 甘地工程技术学院大学; 霍米·巴巴国家研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本综述系统梳理RISC-V在机器学习中的应用现状，提出统一分类法并比较性能权衡，识别能效与工具链进展及标准化等挑战，进而规划四个未来研究方向以巩固其基础平台地位。
AI 中文摘要
开源处理器架构与机器学习的交汇正在推动对可定制、高效且可访问硬件的需求。本综述考察了RISC-V指令集架构在机器学习应用中的现状，基于近期研究分析了其当前能力、挑战和未来方向。分析涵盖了学术与商业实现、软件框架以及实际应用。从指令集扩展和核心实现到编译器优化和部署策略，对RISC-V机器学习生态系统进行了评估。主要贡献包括：RISC-V机器学习实现的统一分类法、性能与设计权衡的比较分析、软件工具链成熟度的评估，以及指令集扩展和专用加速器新兴趋势的识别。研究结果揭示了在能效、专用指令开发和框架集成方面的进展，同时强调了在标准化、验证复杂性和生态系统碎片化方面的挑战。分析提出了四个研究方向以解决当前局限：专用神经处理扩展、自适应和模块化处理器架构、安全框架，以及能效多域架构。这些方向为推进RISC-V作为下一代机器学习系统的基础平台提供了路线图。
英文摘要
The intersection of open-source processor architectures and machine learning is driving the demand for customizable, efficient, and accessible hardware. This survey examines the state of the RISC-V ISA in machine learning applications, analyzing current capabilities, challenges, and future directions based on recent research. The analysis covers academic and commercial implementations, software frameworks, and real-world applications. The RISC-V machine learning ecosystem is evaluated, from instruction set extensions and core implementations to compiler optimizations and deployment strategies. Key contributions include a unified taxonomy of RISC-V ML implementations, a comparative analysis of performance and design trade-offs, an evaluation of software toolchain maturity, and the identification of emerging trends in instruction set extensions and specialized accelerators. Findings reveal progress in energy efficiency, specialized instruction development, and framework integration, while highlighting challenges in standardization, verification complexity, and ecosystem fragmentation. The analysis proposes four research directions to address current limitations: specialized neural processing extensions, adaptive and modular processor architectures, security frameworks, and energy-efficient multi-domain architectures. These directions provide a roadmap for advancing RISC-V as a foundational platform for next-generation machine learning systems.
Journal refThe Journal of Supercomputing, 82(8):424, 2026
DOI：10.1007/s11227-026-08463-z
