Low-precision arithmetic is a key approach in AI coprocessors, offering significant benefits in computational efficiency and power reduction, particularly for edge and resource-constrained applications. However, reduced precision formats like 8-bit and 16-bit introduce challenges, such as rounding and quantization errors, which can lead to accuracy loss. This highlights the need for a robust verification framework to ensure functionality and precision without compromising performance. This paper introduces a verification strategy based on the Universal Verification Methodology (UVM) to address the challenges of low-precision arithmetic in AI hardware. The modular and reusable nature of UVM is leveraged to create a comprehensive verification environment that includes tailored components such as drivers, monitors, scoreboards, sequencers, and transaction classes for low-precision data processing. The framework supports a variety of testing strategies, including random testing and precision-focused corner case analysis, ensuring broad functional coverage. The proposed approach emphasizes precision-specific adaptations, including custom checkers for detecting subtle errors, parameterized verification to handle various precision formats, and detailed coverage metrics for validation. The methodology integrates effectively with AI coprocessor architectures, addressing operational constraints and supporting scalability for more complex designs. Simulation results validate the UVM-based framework’s ability to detect precision-related faults and maintain accuracy, providing a reliable solution for verifying AI coprocessors.