  Weight quantization for deep ConvNets has shown promising results for
applications such as image classification and semantic segmentation and is
especially important for applications where memory storage is limited. However,
when aiming for quantization without accuracy degradation, different tasks may
end up with different bitwidths. This creates complexity for software and
hardware support and the complexity accumulates when one considers
mixed-precision quantization, in which case each layer's weights use a
different bitwidth. Our key insight is that optimizing for the least bitwidth
subject to no accuracy degradation is not necessarily an optimal strategy. This
is because one cannot decide optimality between two bitwidths if one has a
smaller model size while the other has better accuracy. In this work, we take
the first step to understand if some weight bitwidth is better than others by
aligning all to the same model size using a width-multiplier. Under this
setting, somewhat surprisingly, we show that using a single bitwidth for the
whole network can achieve better accuracy compared to mixed-precision
quantization targeting zero accuracy degradation when both have the same model
size. In particular, our results suggest that when the number of channels
becomes a target hyperparameter, a single weight bitwidth throughout the
network shows superior results for model compression.
