Super resolution convolution neural net(SRCNN)relies on neural networks to achieve end-to-end reconstruction from low resolution images to high-resolution images.However,it has the problem of high computational complexity and inability to achieve real-time implementation in practical engineering applications.A denoising neural network acceleration optimization method based on FPGA was designed.By designing a convolutional computing task manager,computing tasks were distributed to the convolutional kernel array,achieving efficient parallel real-time computing.A dual port access mechanism for Block Ram was also proposed,which reduces storage overhead through resource reuse.Based on this acceleration optimization technology,a testing platform was built,and the experimental results showed that the average time required to complete 35×100 image reconstruction under the FPGA main frequency of 250 MHz was 70 ms,which is 10 times faster than OptiPlex 7070.
NETL
NSTL
万方数据
