RISC-V offers a customizable, open alternative to proprietary chips for ML, but realizing its potential requires standardized extensions, better toolchain maturity, and specialized accelerator designs tailored to neural network workloads.
This survey examines how RISC-V, an open-source processor architecture, is being adapted for machine learning workloads. It analyzes existing implementations, software tools, and real-world applications, identifying strengths in energy efficiency and instruction extensions while highlighting challenges like fragmentation and standardization gaps.