用于图像处理的FPGA存储器优化分配

Optimized FPGA memory allocation for image processing

现场可编程门阵列(FPGA)在计算机视觉应用领域有着广阔的前景,然而FPGA有限的片上存储器资源难以满足应用场景下性能、尺寸和功率的需求。针对这个问题,研究片上存储器的资源分配,在最小化片上资源使用和整体功耗的前提下提出一种易于实现的分区平衡算法。实验结果表明,与商用FPGA高级综合工具相比,本文算法的利用率提高达60%,且动态功耗降低了约70%。在高级算法MeanShift跟踪的实验中,实验结果显示,分区算法可以在不影响关键性能的前提下降低总功耗高达30%。

Field Programmable Gate Array(FPGA)has broad prospects in computer vision applications.However,limited memory resources of FPGA are difficult to meet the performance,size and power requirements of the application scenarios.To solve this problem,this paper studies the resource allocation of on-chip memory,designs a partition balancing algorithm to minimize resource usage and power consumption,and implements it on the platform.The experimental results show that,compared with the commercial FPGA's advanced synthesis tools,the proposed algorithm improves the utilization rate by 60%and reduces the dynamic power consumption by up to 70%.In the experiment of the advanced algorithm MeanShift tracking,the experimental data shows that the partition balancing algorithm can reduce the total power consumption by up to 30%without affecting the key performance.