一种用于神经活动的广义拉盖尔 - 沃尔泰拉多输入多输出模型的基于硬件的计算平台。
A hardware-based computational platform for Generalized Laguerre-Volterra MIMO model for neural activities.
作者信息
Li Will X Y, Chan Rosa H M, Zhang Wei, Cheung Ray C C, Song Dong, Berger Theodore W
机构信息
Department of Electronic Engineering, City University of Hong Kong, Hong Kong SAR, China.
出版信息
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:7282-5. doi: 10.1109/IEMBS.2011.6091698.
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 × 10(6)/s, which is 3.1 × 10(3) 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.
本文提出了一种基于现场可编程门阵列（FPGA）和更高级别的自重构平台的并行化和流水线架构，用于对广义拉盖尔 - 沃尔泰拉多输入多输出（Generalized Laguerre-Volterra MIMO）系统进行建模，该系统对于识别尖峰活动背后的时变神经动力学至关重要。我们提出的设计基于赛灵思Virtex-6 FPGA平台，处理核心能够以1.33×10⁶/秒的速度生成数据样本，这比在英特尔i7-860四核处理器上运行的相应C模型快3.1×10³倍。文中介绍了先进自重构平台建设的正在进行的工作，并提供了初步测试结果。
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