A partial binary convolution method suitable for embedded devices

The invention discloses a partial binary convolution method suitable for embedded equipment, belonging to a model compression method of depth learning. The method comprises the following steps: 1. foreach convolution layer of a given CNN, measuring the importance of each convolution core according to the statistics of each output characteristic map; the convolution kernels of each layer being divided into two groups. 2, rearrangeing that two groups of convolution core on the storage space so that the storage positions of the same group of convolution cores are adjacent to each other, recording the rearrange order to generate a new convolution layer; 3, changing the channel order of the convolution nucleus of the next convolution layer according to the rearrangement order of the step 2; Step 4, according to the CNN processed in the above steps, fine-tuning training being carried out, binary quantization being carried out on the convolution layer divided into non-important layers, and the accuracy of the whole