Neural network design and optimization method based on software and hardware joint learning

The invention discloses a neural network design and optimization method based on software and hardware joint learning. The method comprises the following steps: counting a neural network structure rule; carrying out FPGA hardware characteristic prediction; designing an FPGA neural network structure space; and applying a software and hardware joint learning method in a search space, and by combining random search and block supervised search, obtaining a trunk neural network. Based on the design characteristics of the neural network and the hardware characteristics of the FPGA, a search space with prior information is constructed, which is the direction of search establishment; and meanwhile, by combining random search and block supervised search with FPGA model prediction, an efficient neural network model with precision and speed balance is obtained. According to the model, the Top-1 accuracy rate of 77.2% and the speed of 327.67 FPS on an ImageNet data set are achieved on the aspect of ZCU102.