Silent Numerical Failures in On-Device ML Converters: A Systematic Audit of FP16 Overflow in Apple Neural Engine Deployment

Apple's Neural Engine (ANE) executes neural network operations in FP16 arithmetic, making it susceptible to overflow and underflow failures. This work presents a systematic audit of Apple's coremltools converter and identifies five operations that silently produce incorrect results on ANE: softplus, mish, reduce_log_sum_exp, log_softmax, and logcumsumexp. We derive numerically stable implementations, provide quantitative failure analysis, and submit fixes with regression tests to the coremltools repository. The findings impact major model families including YOLO, BERT, ViT, MobileNetV3, and CTC-based speech recognition systems.