  Adversarial data examples have drawn significant attention from the machine
learning and security communities. A line of work on tackling adversarial
examples is certified robustness via randomized smoothing that can provide a
theoretical robustness guarantee. However, such a mechanism usually uses
floating-point arithmetic for calculations in inference and requires large
memory footprints and daunting computational costs. These defensive models
cannot run efficiently on edge devices nor be deployed on integer-only logical
units such as Turing Tensor Cores or integer-only ARM processors. To overcome
these challenges, we propose an integer randomized smoothing approach with
quantization to convert any classifier into a new smoothed classifier, which
uses integer-only arithmetic for certified robustness against adversarial
perturbations. We prove a tight robustness guarantee under L2-norm for the
proposed approach. We show our approach can obtain a comparable accuracy and
4x~5x speedup over floating-point arithmetic certified robust methods on
general-purpose CPUs and mobile devices on two distinct datasets (CIFAR-10 and
Caltech-101).
