Deep convolutional neural network-based inter-layer non-uniform K-means clustering fixed-point quantification method

The invention discloses a deep convolutional neural network-based inter-layer non-uniform K-means clustering fixed-point quantification method. The method includes the following steps that: step 1, a part of images of a deep convolutional neural network which can be correctly identified are selected, and feature mappings (Feature Map) generated in an identification process are extracted; step 2, inter-layer non-regular quantification is performed on the feature mappings in the deep convolutional neural network, and the maximum number of quantification bits of each layer of the convolutional network is determined with the precision of the model maintained; step 3, for each convolutional layer in the model, a K-means clustering algorithm is used to determine fixed-point values satisfying feature mapping distribution, the range of the fixed-point values is made to be located in a range which can be expressed by the maximum number of quantification bits, and the fixed-point values are adopted to represent values