  We introduce a data-free quantization method for deep neural networks that
does not require fine-tuning or hyperparameter selection. It achieves
near-original model performance on common computer vision architectures and
tasks. 8-bit fixed-point quantization is essential for efficient inference on
modern deep learning hardware. However, quantizing models to run in 8-bit is a
non-trivial task, frequently leading to either significant performance
reduction or engineering time spent on training a network to be amenable to
quantization. Our approach relies on equalizing the weight ranges in the
network by making use of a scale-equivariance property of activation functions.
In addition the method corrects biases in the error that are introduced during
quantization. This improves quantization accuracy performance, and can be
applied to many common computer vision architectures with a straight forward
API call. For common architectures, such as the MobileNet family, we achieve
state-of-the-art quantized model performance. We further show that the method
also extends to other computer vision architectures and tasks such as semantic
segmentation and object detection.
