嵌入式异构智能计算系统并行多流水线设计OACSCDCSTPCD

Due to the limited power consumption and the need for real-time intelligent processing of multiple sensors,embedded AI computing systems desire for higher energy efficiency and more parallel intelligent computing services simul-taneously.The digital signal processing architecture DSP+FPGA commonly used in traditional embedded computing sys-tems is not suitable for multiple ANN models inference acceleration.Based on embedded heterogeneous intelligent comput-ing architecture ARM+DLP+SRIO,this paper proposes a parallel multi pipeline design method by taking advantage of the characteristics of multi chip,multi-core and multi memory channels of deep learning processors.Considering the time cost of data transmission,copy,reference and feedback,this method allocates intelligent computing resources for different neu-ral network models to achieve the maximum end-to-end throughput.The experimental results show that the utilization of the deep learning processor using the parallel multi pipeline design method is about 25.2%higher than that of a single pipeline,and about 30.7%higher than that without pipeline.It meets the real-time intelligent processing requirements of visible light,infrared and SAR images,and is valuable for practical applications.