The invention belongs to the technical field of heterogeneous calculation and
image identification, and particularly relates to a
convolutional neural network algorithm design implementation method based on heterogeneous calculation. According to the method, an implemented hardware platform is a Xilinx ZYNQ-7020 programmable SoC (
system on a
chip), an FPGA (
field programmable gate array) and an ARM (advanced RISC
machine) processor are arranged in the hardware platform, an implemented
software platform is an SDSoC, and high-level synthesis and
software definition connecting frame are combinedtogether, so that a HLS (high-level synthesis) result can be seamlessly connected to a
software application. According to the method, a
network model and a training
network model are designed on a PC(
personal computer), a
network model parameter is extracted on the PC, software and hardware code partition is rapidly performed on a
convolutional neural network algorithm on the SDSoC, inputted dataimage preprocessing, a
pooling layer and a classification
algorithm are implemented on an ARM terminal,
convolution operation with maximum calculated amount is mapped to the FPGA and implemented, andperformance and area required by a
system are met. According to the method, a
convolutional neural network algorithm is rapidly implemented by the aid of a heterogeneous platform, the efficiency of the algorithm is greatly improved, and
power consumption is greatly reduced when accuracy of the convolutional algorithm is ensured.