  Large-scale deep neural networks (DNNs) have achieved remarkable success in
many application scenarios. However, high computational complexity and energy
costs of modern DNNs make their deployment on edge devices challenging. Model
quantization is a common approach to deal with deployment constraints, but
searching for optimized bit-widths can be challenging. In this work, we present
Adaptive Bit-Width Quantization Aware Training (AdaQAT), a learning-based
method that automatically optimizes weight and activation signal bit-widths
during training for more efficient DNN inference. We use relaxed real-valued
bit-widths that are updated using a gradient descent rule, but are otherwise
discretized for all quantization operations. The result is a simple and
flexible QAT approach for mixed-precision uniform quantization problems.
Compared to other methods that are generally designed to be run on a pretrained
network, AdaQAT works well in both training from scratch and fine-tuning
scenarios.Initial results on the CIFAR-10 and ImageNet datasets using ResNet20
and ResNet18 models, respectively, indicate that our method is competitive with
other state-of-the-art mixed-precision quantization approaches.
