  As the "Mobile AI" revolution continues to grow, so does the need to
understand the behaviour of edge-deployed deep neural networks. In particular,
MobileNets are the go-to family of deep convolutional neural networks (CNN) for
mobile. However, they often have significant accuracy degradation under
post-training quantization. While studies have introduced quantization-aware
training and other methods to tackle this challenge, there is limited
understanding into why MobileNets (and potentially depthwise-separable CNNs
(DWSCNN) in general) quantize so poorly compared to other CNN architectures.
Motivated to gain deeper insights into this phenomenon, we take a different
strategy and study the multi-scale distributional dynamics of MobileNet-V1, a
set of smaller DWSCNNs, and regular CNNs. Specifically, we investigate the
impact of quantization on the weight and activation distributional dynamics as
information propagates from layer to layer, as well as overall changes in
distributional dynamics at the network level. This fine-grained analysis
revealed significant dynamic range fluctuations and a "distributional mismatch"
between channelwise and layerwise distributions in DWSCNNs that lead to
increasing quantized degradation and distributional shift during information
propagation. Furthermore, analysis of the activation quantization errors show
that there is greater quantization error accumulation in DWSCNN compared to
regular CNNs. The hope is that such insights can lead to innovative strategies
for reducing such distributional dynamics changes and improve post-training
quantization for mobile.
