Aiming at the problems of high complexity of convolution operation

large amount of calculation and the limitation of delay and power consumption when the algorithm is calculated on the CPU and GPU in the convolutional neural network

from the perspective of increasing the calculation rate and reducing power consumption of existing hardware platforms

a reconfigurable neural network acceleration system with high throughput and low power consumption based on ZYNQ is presented. In order to make full use of computing resources

a convolution operation loop optimization circuit is explored; in order to reduce the bandwidth access

a special arrangement of the data in memory is designed. Taking the VGG16 network as an example

using ZYNQ to accelerate the system

62.00 GPOS effective computing power was reached

which was 2.58 times and 6.88 times that of the GPU and CPU respectively. Its MAC utilization rate was as high as 98.20%

which was close to the theoretical value of the Roofline model. The computing power consumption of the accelerator was 2.0W

and the energy efficiency ratio was 31.00GOPS/W

which was 112.77 times that of the GPU and 334.41 times that of the CPU.