  Deep neural network quantization with adaptive bitwidths has gained
increasing attention due to the ease of model deployment on various platforms
with different resource budgets. In this paper, we propose a meta-learning
approach to achieve this goal. Specifically, we propose MEBQAT, a simple yet
effective way of bitwidth-adaptive quantization aware training (QAT) where
meta-learning is effectively combined with QAT by redefining meta-learning
tasks to incorporate bitwidths. After being deployed on a platform, MEBQAT
allows the (meta-)trained model to be quantized to any candidate bitwidth then
helps to conduct inference without much accuracy drop from quantization.
Moreover, with a few-shot learning scenario, MEBQAT can also adapt a model to
any bitwidth as well as any unseen target classes by adding conventional
optimization or metric-based meta-learning. We design variants of MEBQAT to
support both (1) a bitwidth-adaptive quantization scenario and (2) a new
few-shot learning scenario where both quantization bitwidths and target classes
are jointly adapted. We experimentally demonstrate their validity in multiple
QAT schemes. By comparing their performance to (bitwidth-dedicated) QAT,
existing bitwidth adaptive QAT and vanilla meta-learning, we find that merging
bitwidths into meta-learning tasks achieves a higher level of robustness.
