Mixed-precision networks allow for a variable bit-width quantization for
every layer in the network. A major limitation of existing work is that the
bit-width for each layer must be predefined during training time. This allows
little flexibility if the characteristics of the device on which the network is
deployed change during runtime. In this work, we propose Bit-Mixer, the very
first method to train a meta-quantized network where during test time any layer
can change its bid-width without affecting at all the overall network's ability
for highly accurate inference. To this end, we make 2 key contributions: (a)
Transitional Batch-Norms, and (b) a 3-stage optimization process which is shown
capable of training such a network. We show that our method can result in mixed
precision networks that exhibit the desirable flexibility properties for
on-device deployment without compromising accuracy. Code will be made
available.

本文提出了 Bit-Mixer 方法，为高度精准预测训练多量化层的混合精度网络，在测试期间任何层都可以改变自己的比特宽度，并通过 “转换批量归一化” 和 3 阶段优化，展示了网络的训练过程以及具有理想的灵活性属性的混合精度网络可供设备部署，不会影响推断准确度。

Bit-Mixer: 运行时位宽选择的混合精度网络

Bit-Mixer: Mixed-precision networks with runtime bit-width selection

Low-precision networks, with weights and activations quantized to low
bit-width, are widely used to accelerate inference on edge devices. However,
current solutions are uniform, using identical bit-width for all filters. This
fails to account for the different sensitivities of different filters and is
suboptimal. Mixed-precision networks address this problem, by tuning the
bit-width to individual filter requirements. In this work, the problem of
optimal mixed-precision network search (MPS) is considered. To circumvent its
difficulties of discrete search space and combinatorial optimization, a new
differentiable search architecture is proposed, with several novel
contributions to advance the efficiency by leveraging the unique properties of
the MPS problem. The resulting Efficient differentiable MIxed-Precision network
Search (EdMIPS) method is effective at finding the optimal bit allocation for
multiple popular networks, and can search a large model, e.g. Inception-V3,
directly on ImageNet without proxy task in a reasonable amount of time. The
learned mixed-precision networks significantly outperform their uniform
counterparts.

该研究旨在解决低精度网络中过滤器位宽不同敏感性也不同的问题，提出了一种新型可微分搜索架构 ——Efficient differentiable Mixed-Precision network Search（EdMIPS），采用该方法能够高效地寻找多种流行网络最佳位分配，且搜索速度快，学习到的混合精度网络性能显著优于其均匀位宽网络。