Why Some Models Quantize Better Than Others (ym2132.github.io)

A recent analysis has revealed that not all neural network models handle quantization equally, specifically highlighting the differences between EfficientNetB0 (ENB0) and ResNet18 (RN18). In testing, RN18 demonstrated a significantly better quantization performance, maintaining acceptable accuracy levels even when reduced to INT8 (8-bit integers) from FP32 (32-bit floating point), while ENB0's accuracy plummeted from approximately 90% to 34% after quantization. The reason for this disparity lies in the range of activation values; ENB0 exhibits a larger range, resulting in increased quantization error when values are mapped to the limited INT8 space. This finding is particularly significant for the AI/ML community as it underscores the necessity of inspecting activation value ranges post-training when quantization is anticipated. As models like ENB0 may struggle with efficient quantization due to their architecture, developers must carefully consider model choice and the calibration methodologies used—static, dynamic, or quantization aware training—to optimize performance. The insights from this case study suggest that model architecture fundamentally influences quantization success, guiding practitioners to prioritize models with more suitable activation value distributions for effective deployment in real-world applications.