Developing a Reconfigurable On-Line Modeling Platform

开发可重构在线建模平台

• 批准号: 6689696
• 负责人: Ross Kenneth Snider
• 金额: $ 14.98万
• 依托单位: HYLITECH
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-08-21 至 2005-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6689696
• 关键词: biological signal transduction; computational neuroscience; computer data analysis; computer program /software; computer simulation; computer system design /evaluation; digital imaging; neural information processing; neural transmission; neurophysiology; online computer; supercomputer; time resolved data

DESCRIPTION (provided by applicant): This proposal is being sent in response to NIMH PA-00-118. The objective of this proposal is to begin the commercialization process of a high performance reconfigurable signal-processing platform that will perform real-time on-line analysis of large-scale multi-channel data streams for data-driven neural simulations and modeling. The platform will be used to aid the discovery process where the cooperative neural encoding schemes through which sensory information is represented and transmitted within a nervous system will be uncovered. The system will enable real-time decoding of the neural information streams, and will enable experimental perturbation of the encoded information while the neural signals are in transit between peripheral and central processing stages. This will provide an unprecedented degree of interactive control in the analysis of neural function, and could lead to major insights into the biological basis of neural computation. The aim of this Phase I proposal is to construct a computation node using high performance field programmable gate arrays (FPGA) to be used in a data-driven modeling platform and to develop the associated software necessary to implementing neural models (mapping algorithms to hardware) efficiently in high end FPGA devices to enable real-time modeling & simulations. This will involve developing prototype boards comprised of Xilinx's Virtex-II Pro FPGAs with adequate communication bandwidth between nodes to ensure real-time performance. Several implementation techniques will be explored to discover the most efficient method of implementing neural modeling algorithms in hardware. These methods include: 1.) Using Mathwork's Matlab/Simulink and Xilinx's System Generator to map Simulink block diagrams to hardware. 2.) Using Xilinx's FORGE compiler to translate JAVA code to hardware. 3.) Using Celoxica's C Compiler to translate C code to hardware. 4.) Developing and embedding custom neural microprocessors in FPGAs.

描述（由申请人提供）：本提案是为了回应 NIMH PA-00-118 而发送的。该提案的目标是开始高性能可重构信号处理平台的商业化进程，该平台将对大规模多通道数据流进行实时在线分析，以进行数据驱动的神经模拟和建模。该平台将用于帮助发现过程，其中将揭示神经系统内表示和传输感觉信息的协作神经编码方案。该系统将能够对神经信息流进行实时解码，并在神经信号在外围和中央处理阶段之间传输时对编码信息进行实验扰动。这将为神经功能分析提供前所未有的交互控制程度，并可能带来对神经计算生物学基础的重大见解。该第一阶段提案的目的是使用高性能现场可编程门阵列 (FPGA) 构建一个用于数据驱动建模平台的计算节点，并开发在高端 FPGA 设备中高效实施神经模型（将算法映射到硬件）所需的相关软件，以实现实时建模和仿真。这将涉及开发由 Xilinx Virtex-II Pro FPGA 组成的原型板，并在节点之间提供足够的通信带宽，以确保实时性能。将探索几种实现技术，以发现在硬件中实现神经建模算法的最有效方法。这些方法包括： 1.) 使用Mathwork 的Matlab/Simulink 和Xilinx 的System Generator 将Simulink 框图映射到硬件。 2.) 使用Xilinx 的FORGE 编译器将JAVA 代码翻译为硬件。 3.) 使用 Celoxica 的 C 编译器将 C 代码翻译为硬件。 4.) 开发定制神经微处理器并将其嵌入到 FPGA 中。