Data-Free Quantization Through Weight Equalization and Bias Correction

Markus Nagel, Mart van Baalen, Tijmen Blankevoort, Max Welling

Abstract:

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.