  Mixed-precision quantization has received increasing attention for its
capability of reducing the computational burden and speeding up the inference
time. Existing methods usually focus on the sensitivity of different network
layers, which requires a time-consuming search or training process. To this
end, a novel mixed-precision quantization method, termed CSMPQ, is proposed.
Specifically, the TF-IDF metric that is widely used in natural language
processing (NLP) is introduced to measure the class separability of layer-wise
feature maps. Furthermore, a linear programming problem is designed to derive
the optimal bit configuration for each layer. Without any iterative process,
the proposed CSMPQ achieves better compression trade-offs than the
state-of-the-art quantization methods. Specifically, CSMPQ achieves 73.03$\%$
Top-1 acc on ResNet-18 with only 59G BOPs for QAT, and 71.30$\%$ top-1 acc with
only 1.5Mb on MobileNetV2 for PTQ.
