Flexible Architecture for Simulation and Testing (FAST)

本文介绍了一种名为FAST的灵活多线程原型系统,它结合了固定功能处理器和FPGA技术,旨在提供一个硬件平台用于多线程微处理器架构的研究。FAST能够在接近硬件速度的情况下运行,并支持细粒度和推测性多线程,同时具备现代CMP架构的延迟和带宽特性。

We are at the point in the area of multithreaded microprocessor architectures where further progress will require the development of a hardware prototype. This prototype should support more than two parallel threads and thread-level speculation (TLS). Currently, no commercial microprocessor has these multithreading capabilities and this prevents the serious OS, compiler and application development that is required to take full advantage of multiple threads and TLS. Without the resulting optimized software, it will be difficult to understand the true benefits of these capabilities or make the appropriate hardware/software design tradeoffs to achieve the best performance. However, the problem with building a microprocessor is that it requires VLSI chip design, a resource-intensive process. The immense task of chip design verification before tape out, in particular, can make microprocessor design a difficult undertaking in an academic environment.  This is the primary reason that all prior multithreading and TLS research has relied on software simulators.  

 

FAST is a flexible simulation platform that will enable chip multiprocessor (CMP) and multithreaded simulation on a real hardware platform that enables complex system design with the ability to execute millions of instructions per second.  FAST is a flexible platform that enables the manipulation of the memory hierarchy and other key components.  FPGAs are used to interface between the 4 processor tiles and within the processor tiles.  Figure 1 below, illustrates the FAST PCB at a high level.  The yellow tiles are processor tiles and the blue tile servers as the internal and external system interconnect.  The initial implementation of the FAST leverages the existing Hydra Architecture components, but other CMP designs can be realized by changing the FPGA configuration.

 

Figure 1: Generic FAST implementation on a PCB.

 

Figure 2 shows an expanded view of the processor tiles.  Each tile consists of an FPU, CPU, L1 memory, and FPGAs.  This configuration enables the processing tile to run both floating point and integer applications, while giving it the flexibility to modify the L1 memory configuration and adding other components, like multithreading support and profiling metrics, via the FPGAs

 

Figure 2: Expanded view of the FAST processor tile.

 

We believe it is possible to build a flexible research prototype using fixed-function processors together with FPGAs without doing any VLSI design. We intend to demonstrate this by building a flexible CMP prototyping environment with existing chips called FAST. The key idea is that by combining ten-year-old microprocessor chips with state-of-the-art FPGA chips, it is possible to build a single-board multiprocessor prototyping environment that provides support for TLS and operates at hardware speeds, yet has the latency and bandwidth characteristics equivalent to a modern CMP architecture.  Figure 3 below, is a simplified scaled drawing of the FAST PCB.  A processor tile occupies each corner of the FAST PCB.  The L2 memory, L2 memory controller, Read/Write controller and internal and external glue components occupy the center of the FAST PCB.

 

Figure 3: Simplified component layout for the FAST PCB.

 

As Figure 2 illustrate the processor tile components, Figure 3 uses the same color coding to differentiate the CPU (red), FPU (green), FPGAs (purple) and L1 memory (yellow).  In the center of the PCB resides the L2 memory in light blue and all the internal and external glue components in dark blue.  Starting from the top of the PCB in the center, there is the embedded Ethernet Board that enables the external communication interface.  Next to that is the CPLD that provides more glue logic to augment the capabilities of the microcontroller on the embedded Ethernet board. Below these components are the 2 XC2V6000 which controller the L2 memory and external memory interfaces.  The power connector and DC-to-DC voltage regulators are shown in orange. The voltage regulators are labeled with their output voltages that supply the core FPGA voltages.

 


Figure 4: Completed FAST PCB with component labels.


FAST PCB Specification Files 
FAST Complete Software Archive 

The overall goals of the FAST project is to develop a hardware prototype system that allows hardware and software experimentation with fine-grain and speculative multithreading.  More specifically:

 

Hardware Goals:

  • Explore spectrum of variation in multithreading architectures exploiting the flexibility of the FPGAs.
  • Explore alternative ways to use multithreaded hardware, e.g., support fault tolerance.

 

Software Goals:

  • Explore the design of OS, programming environments, programming paradigms and applications
  • Determine full potential for speedup provided by a TLS architecture for general purpose applications.

 

Education Goals:

  • Provide a project development environment for advance digital design and computer architecture classes

 

Grants & Donations

This project is supported by NSF grant # CCR-0220138, as well as donations from Xilinx, Inc.

\begin{table*}[htbp] \centering \caption{Tools employed in discipline model tool chain.} \label{table2} \begin{tabularx}{\textwidth}{c p{2cm} c p{3cm} X} \toprule \textbf{No.} & \textbf{Name} & \textbf{License} & \textbf{Usage} & \textbf{Description} \\ \midrule \textbf{1} & robotics toolbox\tablefootnote{robotics toolbox:\url{https://github.com/petercorke/robotics-toolbox-python}} & MIT & Workspace calculation, force and torque calculation, path planning, etc. & The Toolbox provides tools for representing the kinematics and dynamics of serial-link manipulators. \\ \textbf{2} & CadQuery\tablefootnote{CadQuery:\url{https://github.com/CadQuery/cadquery}} & Apache2.0 & Build 3D models of parts and assemblies. & CadQuery is an intuitive, easy-to-use Python module for building parametric 3D CAD models. \\ \textbf{3} & Exudyn\tablefootnote{Exudyn:\url{https://github.com/jgerstmayr/EXUDYN}} & EXUDYN General & Multibody system calculation. & A flexible multibody dynamics systems simulation code with Python. \\ \textbf{4} & Isaac Sim\tablefootnote{Isaac Sim:\url{https://github.com/isaac-sim/IsaacSim}} & Apache 2.0 & Algorithm testing, virtual verification. & A simulation platform designed to develop and test robots in realistic virtual environments. \\ \textbf{5} & ROS2\tablefootnote{ROS2:\url{https://github.com/ros2}} & Apache 2.0 & Algorithm and communication. & A set of software libraries and tools that helps to build robot applications. \\ \textbf{6} & Dash\tablefootnote{Dash:\url{https://github.com/plotly/dash}} & MIT & Connecting architecture model and discipline models. & A web service for packaging analytical Python code.\\ \bottomrule \end{tabularx} \end{table*} 看我的代码 请帮我修复
08-22
MATLAB代码实现了一个基于多种智能优化算法优化RBF神经网络的回归预测模型,其核心是通过智能优化算法自动寻找最优的RBF扩展参数(spread),以提升预测精度。 1.主要功能 多算法优化RBF网络:使用多种智能优化算法优化RBF神经网络的核心参数spread。 回归预测:对输入特征进行回归预测,适用于连续值输出问题。 性能对比:对比不同优化算法在训练集和测试集上的预测性能,绘制适应度曲线、预测对比图、误差指标柱状图等。 2.算法步骤 数据准备:导入数据,随机打乱,划分训练集和测试集(默认7:3)。 数据归一化:使用mapminmax将输入和输出归一化到[0,1]区间。 标准RBF建模:使用固定spread=100建立基准RBF模型。 智能优化循环: 调用优化算法(从指定文件夹中读取算法文件)优化spread参数。 使用优化后的spread重新训练RBF网络。 评估预测结果,保存性能指标。 结果可视化: 绘制适应度曲线、训练集/测试集预测对比图。 绘制误差指标(MAE、RMSE、MAPE、MBE)柱状图。 十种智能优化算法分别是: GWO:灰狼算法 HBA:蜜獾算法 IAO:改进天鹰优化算法,改进①:Tent混沌映射种群初始化,改进②:自适应权重 MFO:飞蛾扑火算法 MPA:海洋捕食者算法 NGO:北方苍鹰算法 OOA:鱼鹰优化算法 RTH:红尾鹰算法 WOA:鲸鱼算法 ZOA:斑马算法
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