  In recent years Deep Neural Networks (DNNs) have been rapidly developed in
various applications, together with increasingly complex architectures. The
performance gain of these DNNs generally comes with high computational costs
and large memory consumption, which may not be affordable for mobile platforms.
Deep model quantization can be used for reducing the computation and memory
costs of DNNs, and deploying complex DNNs on mobile equipment. In this work, we
propose an optimization framework for deep model quantization. First, we
propose a measurement to estimate the effect of parameter quantization errors
in individual layers on the overall model prediction accuracy. Then, we propose
an optimization process based on this measurement for finding optimal
quantization bit-width for each layer. This is the first work that
theoretically analyse the relationship between parameter quantization errors of
individual layers and model accuracy. Our new quantization algorithm
outperforms previous quantization optimization methods, and achieves 20-40%
higher compression rate compared to equal bit-width quantization at the same
model prediction accuracy.
