  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.
