A hardware-based computational platform for Generalized Laguerre-Volterra MIMO model for neural activities
A hardware-based computational platform for Generalized Laguerre-Volterra MIMO model for neural activities
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DOI：
10.1109/iembs.2011.6091698
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
Will X. Y. Li;Rosa H. M. Chan;Wei Zhang;R. Cheung;D. Song;T. Berger
中科院分区：
文献类型：
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作者：
Will X. Y. Li;Rosa H. M. Chan;Wei Zhang;R. Cheung;D. Song;T. Berger
本文提出了一种基于FPGA的并行流水线结构和一个更高层次的自重构平台来建模广义Laguerre-Volterra MIMO系统，该系统是识别时变神经动力学的关键。该设计基于Xilinx Virtex-6 FPGA平台，处理核的数据采样速度为1.33×106/s，比在Intel i7-860四核处理器上运行的相应C模型快3.1×103倍。介绍了先进的自重构平台的建设正在进行的工作，并提供了初步的测试结果。
A parallelized and pipelined architecture based on FPGA and a higher-level Self Reconfiguration Platform are proposed in this paper to model Generalized Laguerre-Volterra MIMO system essential in identifying the time-varying neural dynamics underlying spike activities. Our proposed design is based on the Xilinx Virtex-6 FPGA platform and the processing core can produce data samples at a speed of 1.33×106/s, which is 3.1×103 times faster than the corresponding C model running on an Intel i7–860 Quad Core Processor. The ongoing work of the construction of the advanced Self Reconfiguration Platform is presented and initial test results are provided.