When developing deep neural networks, utilizing small data types—such as FP16 or INT8 formats—can lead to numerical overflow, where computed values exceed the maximum limit representable by that specific precision. This computational issue is primarily a significant problem during the model training phase, though it can also occasionally impact model inference to a lesser extent.