  Post-training quantization is a representative technique for compressing
neural networks, making them smaller and more efficient for deployment on edge
devices. However, an inaccessible user dataset often makes it difficult to
ensure the quality of the quantized neural network in practice. In addition,
existing approaches may use a single uniform bit-width across the network,
resulting in significant accuracy degradation at extremely low bit-widths. To
utilize multiple bit-width, sensitivity metric plays a key role in balancing
accuracy and compression. In this paper, we propose a novel sensitivity metric
that considers the effect of quantization error on task loss and interaction
with other layers. Moreover, we develop labeled data generation methods that
are not dependent on a specific operation of the neural network. Our
experiments show that the proposed metric better represents quantization
sensitivity, and generated data are more feasible to be applied to
mixed-precision quantization.
