Research on Heterogeneous Acceleration of Deep Learning Method for Missile-Borne Image Processing

Abstract

The problem existing in the transformation of deep learning algorithm to engineering application is analyzed. Combining with the characteristics and development trends of army intelligent ammunition, the missile-borne image processing heterogeneous accelerate system for deep learning is put forward based on the research of compression, quantitative and hardware heterogeneous acceleration, realizing heterogeneous hardware design. The DNNDK is used to compress and quantify the Yolo v3 model. The weight and parameter compression rate are more than 90% and 80%, realizing the lightweight design of Yolo v3. Based on the DPU hardware acceleration architecture, the algorithm is transplanted to the missile-borne embedded platform, and its power consumption and detection efficiency meet the requirements of missile-borne image processing.

Keywords

• |missile-borne image|deep learning|fpga|systolic array|winograd convolution