基于自组织地图的定位方案在FPGA上的实现

Deng Li, Yonggang Wang, Liwei Wang, Xinyi Cheng
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引用次数: 3

摘要

针对连续晶体PET探测器,提出了一种基于自组织图(SOM)神经网络的定位方案,该方案平均分辨率为2.07mm,可在现场可编程门阵列(FPGA)上实现。在本文中,我们提出了SOM方案的FPGA设计,并将其应用于实验数据。该算法的实现利用流水线和并行结构,在系统时钟运行频率为200M Hz的情况下,每秒可处理5M个事件。测试结果表明,FPGA解决方案具有与软件平台基本相当的性能。考虑到利用SOM方案确定DOI的潜力,未来有望实现实时三维位置估计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implementation of Self-organizing Map Based Positioning Scheme on FPGA
For continuous crystal based PET detector, we develop a Self-organizing Map (SOM) Neural Network Based Positioning Scheme which can achieve 2.07mm average resolution and is feasible for implementation on Field Programmable Gate Array (FPGA). In this paper, we propose the FPGA design of SOM scheme and apply it to our experiment data. Taking advantage of the pipelined and parallel structure, the implementation of this algorithm is able to process 5M events per second with system clock running at 200M Hz. The test results show that the FPGA solution has almost the equal performance with software platform. Considering the potentiality of DOI determination using SOM scheme, it is promising to realize real-time 3D position estimation in the future.
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