  Quantization has become a mainstream compression technique for reducing model
size, computational requirements, and energy consumption for modern deep neural
networks (DNNs). With improved numerical support in recent hardware, including
multiple variants of integer and floating point, mixed-precision quantization
has become necessary to achieve high-quality results with low model cost. Prior
mixed-precision methods have performed either a post-training quantization
search, which compromises on accuracy, or a differentiable quantization search,
which leads to high memory usage from branching. Therefore, we propose the
first one-shot mixed-precision quantization search that eliminates the need for
retraining in both integer and low-precision floating point models. We evaluate
our search (FLIQS) on multiple convolutional and vision transformer networks to
discover Pareto-optimal models. Our approach improves upon uniform precision,
manual mixed-precision, and recent integer quantization search methods. With
integer models, we increase the accuracy of ResNet-18 on ImageNet by 1.31% and
ResNet-50 by 0.90% with equivalent model cost over previous methods.
Additionally, for the first time, we explore a novel mixed-precision
floating-point search and improve MobileNetV2 by up to 0.98% compared to prior
state-of-the-art FP8 models. Finally, we extend FLIQS to simultaneously search
a joint quantization and neural architecture space and improve the ImageNet
accuracy by 2.69% with similar model cost on a MobileNetV2 search space.
