The invention discloses a Zynq-based
convolutional neural network hardware acceleration system, which is realized based on a Zynq heterogeneous
chip and comprises a PL end module, a PS end module and a storage module. The PL end module comprises a
convolution acceleration module, a full connection layer acceleration module and a Softmax activation module and is used for completing
convolution calculation, full connection operation and
activation function processing; the PS end module comprises an input
data processing module and a post-
processing module and is responsible for loading and preprocessing input data and sorting and screening detection results. The storage module is composed of an on-
chip memory and an off-
chip memory, wherein the on-chip memory stores an intermediate
processing result of input data, a
convolution kernel and intermediate feature map data; and the off-chip memory stores the original image data, the weight parameter of the full connection layer and the final detection result. All the modules are connected through a 64-bit AXI
bus, the PS end module is responsible for coordinating data flow, and the PL end module executes efficient
parallel computing. The
system makes full use of the high-performance characteristic of the Zynq heterogeneous architecture, is suitable for an efficient reasoning acceleration scene of the
convolutional neural network, and particularly has a wide application prospect in embedded AI equipment.