AI Applications on RISC-V and ARM Architectures: A Focus on Fog Computing, Edge Computing, and IoT

: This paper presents a comprehensive survey of RISC-V and ARM architecture applications for AI workloads across fog computing, edge computing, and IoT domains. We analyze recent developments (2020-2025) in instruction set extensions, neural processing units, and software frameworks that enable efficient machine learning inference on resource-constrained devices. Our comparative analysis reveals that RISC-V offers superior energy efficiency (up to 4 × lower power consumption) and architectural flexibility through custom ISA extensions, while ARM maintains advantages in ecosystem maturity, raw inference performance (up to 15 × faster for large networks), and commercial deployment readiness. The survey covers AI workloads spanning computer vision, natural language processing, anomaly detection, and sensor fusion, with a particular focus on TinyML implementations that achieve sub-milliwatt operation. We identify emerging trends, including in-memory computing, neuromorphic architectures, and heterogeneous computing approaches that combine CPU cores with domain-specific accelerators. Our findings indicate that architecture selection critically depends on application requirements, with RISC-V preferred for power-constrained custom acceleration and ARM for performance-critical deployments requiring mature toolchain support.