We present any-precision deep neural networks (DNNs), which are trained with
a new method that allows the learned DNNs to be flexible in numerical precision
during inference. The same model in runtime can be flexibly and directly set to
different bit-widths, by truncating the least significant bits, to support
dynamic speed and accuracy trade-off. When all layers are set to low-bits, we
show that the model achieved accuracy comparable to dedicated models trained at
the same precision. This nice property facilitates flexible deployment of deep
learning models in real-world applications, where in practice trade-offs
between model accuracy and runtime efficiency are often sought. Previous
literature presents solutions to train models at each individual fixed
efficiency/accuracy trade-off point. But how to produce a model flexible in
runtime precision is largely unexplored. When the demand of efficiency/accuracy
trade-off varies from time to time or even dynamically changes in runtime, it
is infeasible to re-train models accordingly, and the storage budget may forbid
keeping multiple models. Our proposed framework achieves this flexibility
without performance degradation. More importantly, we demonstrate that this
achievement is agnostic to model architectures and applicable to multiple
vision tasks. Our code is released at
this https URL

提出了一种新方法训练任意精度深度神经网络（DNNs），可以在推断时灵活地设置数字精度，从而支持动态速度和准确度的折衷，并且该方法不会降低性能，该方法适用于多个视觉任务和模型结构