  Computationally expensive neural networks are ubiquitous in computer vision
and solutions for efficient inference have drawn a growing attention in the
machine learning community. Examples of such solutions comprise quantization,
i.e. converting the processing values (weights and inputs) from floating point
into integers e.g. int8 or int4. Concurrently, the rise of privacy concerns
motivated the study of less invasive acceleration methods, such as data-free
quantization of pre-trained models weights and activations. Previous approaches
either exploit statistical information to deduce scalar ranges and scaling
factors for the activations in a static manner, or dynamically adapt this range
on-the-fly for each input of each layers (also referred to as activations): the
latter generally being more accurate at the expanse of significantly slower
inference. In this work, we argue that static input quantization can reach the
accuracy levels of dynamic methods by means of a per-channel input quantization
scheme that allows one to more finely preserve cross-channel dynamics. We show
through a thorough empirical evaluation on multiple computer vision problems
(e.g. ImageNet classification, Pascal VOC object detection as well as
CityScapes semantic segmentation) that the proposed method, dubbed SPIQ,
achieves accuracies rivalling dynamic approaches with static-level inference
speed, significantly outperforming state-of-the-art quantization methods on
every benchmark.
