  Quantization for deep neural networks (DNN) have enabled developers to deploy
models with less memory and more efficient low-power inference. However, not
all DNN designs are friendly to quantization. For example, the popular
Mobilenet architecture has been tuned to reduce parameter size and
computational latency with separable depth-wise convolutions, but not all
quantization algorithms work well and the accuracy can suffer against its float
point versions. In this paper, we analyzed several root causes of quantization
loss and proposed alternatives that do not rely on per-channel or
training-aware approaches. We evaluate the image classification task on
ImageNet dataset, and our post-training quantized 8-bit inference top-1
accuracy in within 0.7% of the floating point version.
