RISC-V 架构在机器学习领域的调研报告
RISC-V and machine learning: a survey
想了解 RISC-V 架构在机器学习领域的最新进展和未来方向，这篇论文提供了系统性的分析。
这篇论文对 RISC-V 架构在机器学习应用中的现状进行了全面分析，涵盖了学术和商业实现、软件框架以及实际应用。它评估了 RISC-V 的机器学习生态系统，从指令集扩展和核心实现到编译器优化和部署策略。研究发现，在能效、专用指令开发以及框架集成方面取得了进展，但也指出了标准化、验证复杂性和生态系统碎片化等挑战。
RISC-V and machine learning: a survey
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.