RISC-V powers advances in customizable machine learning hardware
RISC-V and machine learning: a survey
Machine LearningHardware Architecture
Summary
There is growing interest in making computer chips that can be easily changed and are good at running machine learning tasks. The authors looked at how the open-source RISC-V chip design is used for machine learning, including different hardware designs, software, and real use cases. They found progress in making these chips use less energy and work better with machine learning tools, but also discovered problems with how these designs are standardized and tested. They suggest new research directions to improve RISC-V for future machine learning needs, like specialized extensions and better security.
What this means in practice
- •For chip designers: Design customizable processors tailored for efficient machine learning with RISC-V instruction extensions and modular architectures discussed in this paper.
- •For compiler engineers: Improve compiler toolchains for RISC-V processors to better support machine learning workloads through insights on current software ecosystem maturity.
A survey. It maps existing work.
Authors
Shriman Keshri, Apparna Singh, Chinmaya Kumar Palo, Shreya Adya, Subhankar Mishra
Abstract
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.