Application of many-core processing architecture to echo detection of phase-coded pulse for autonomous underwater vehicle

With the development of new target detection technology such as wideband coded pulse and multipleinput multiple output, the computational complexity and memory space requirement increase seriously. To address this problem, basing on the characteristic of data parallelism of echo detection algorithm of phase-coded pulse for Autonomous Underwater Vehicle (AUV), a strategy of using Graphics Processing Unit (GPU) with many-core processing architecture is proprosed. And a GPU parallelism implementation framework of the alogrithm is designed by considering a series of optimization measures including task allocation strategy, data processing procedure, GPU occupancy and memory access pattern. The desktop GPU platform, embedded GPU platform and traditional signal processing platform of AUV based on multicore Digital Signal Processor (DSP) are tested by experimental data, and the result shows that the embedded GPU platform is more dominant than multicore DSP platform in power consumption and computing performance. The research results indicate that the embedded GPU platform can greatly improve the performace per watt and simplify the system design, and it can meet the requirement of large dataset, low power consumption, and real time for AUV detection system.