基于虫孔交换的NoC映射和通讯参数自动化设计方法研究

NoC映射和通讯参数设计是NoC设计过程中非常重要的部分,其结果直接影响NoC的性能、面积和功耗。本文将NoC映射问题和通讯参数设计问题统一考虑,首先对NoC映射问题进行了形式化定义,然后提出了基于虫孔交换的NoC延迟性能分析方法,根据应用的通讯延迟约束,将应用模型映射到NoC拓扑结构上,并自动设计出NoC通讯参数。实验表明,本文所提出的延迟性能分析方法比以往方法精确7%~13%,映射结果和通讯参数设计更优。     

基于路由器解析式模型的NoC网络性能分析方法

建立一种高效的片上网络(NoC)性能分析方法对NoC早期的系统设计分析具有重要的指导意义.首先从NoC路由器工作原理出发,对报文传输中的各种阻塞现象进行分析,建立了基于M/G/1/N排队系统的路由器模型;然后提出NoC网络性能分析算法,并且给出了传输延迟、饱和吞吐率等参数的解析表达式.与时钟精度仿真结果比较表明,该方法分析误差约为6.9%,但分析效率提高了约200倍.该方法适用于指导程序NoC拓扑映射,在获取最优映射方案同时,可有效地挖掘网络通信瓶颈.     

NoC在纳米计算结构下的延迟与拥塞优化映射

片上网络(NoC)映射的性能严重依赖于该网络拥有的互联结构.在自组装的纳米计算结构上探索了NoC映射问题,特别研究了在该结构上与最小延迟与拥塞相关的映射问题.提出了一个用于评估传输延迟的模型,并提出了SWMAP映射算法,使应用任务能够映射到具有小世界特性的纳米计算结构上.该算法使用了两种应用任务测试样例进行测试,实验结...     

基于蚁群优化算法的NoC映射

功耗问题正逐渐成为NoC领域的研究热点,很多研究人员都在研究NoC功耗最小化的设计技术。文章采用一种有效的蚁群优化算法实现了NoC映射：在自动映射处理单元的同时,尽可能地减少了系统的通讯功耗。实验结果表明采用蚁群优化算法可以很快地收敛;针对不同的应用,可以减少25％-70％通讯功耗。     

基于云自适应遗传算法的NoC路径分配研究

路径分配是NoC设计流程中的两个关键步骤之一;路径分配的结果对NoC系统的性能尤其是通讯延时有着很重要的影响;多约束条件下的NoC路径分配问题是NP完全问题,要求出其最优解比较困难,目前常用的方法是利用启发式算法求得其较优解;文中提出一种基于云自适应遗传算法的NoC路径分配解决方案,该算法利用云模型对传统遗传算法加以改进,采取新的方法自动调整遗传算法过程中的交叉概率pc和变异概率pm,将适应度与云模型的3个参数Ex、En、He相互结合,从而达到优化遗传算法的目的;将此算法应用于2D-Mesh拓扑结构的NoC中,以平衡链路负载和联合优化为实验目标,以优化静态通讯分配结果;实验证明,文章所采取的算法在平衡链路负载和联合优化方面均取得了良好的效果。     

基于自适应模拟退火的NoC映射算法

在片上网络(Network on Chip,NoC)系统中,如何完成应用特征图到结构特征图的映射是影响系统实际性能的关键步骤之一。针对NoC系统越发庞大,映射算法耗时也随之增加的问题,提出了自适应模拟退火(Self-Adaptive Simulated Annealing,SASA)的NoC映射算法。采用相对平滑方式实现温度下降过程,针对模拟退火算法易陷于局部最优的缺点,采用自适应方法改变新解生成方式,提高了算法收敛于全局最优的概率。实验结果表明,该算法与常见NoC映射算法(如基于遗传的映射算法)相比,平均性能提升了5.3%,耗时缩短了11.1%。     

基于云自适应遗传算法的NoC映射研究

NoC映射是NoC设计中的重要步骤,映射结果的优劣对NoC的QoS约束和通信功耗有着很大的影响。提出一种采用云自适应遗传算法实现NoC映射的方案,该算法利用云模型对传统遗传算法加以改进,以此新方法自动调整遗传算法过程中的交叉概率和变异概率,从而达到优化遗传算法的目的。结合NoC映射中的具体问题,在功耗和延时约束的限制条件下,建立了延时约束下的NoC映射功耗数学模型。实验表明,该方法在NoC映射中取得了良好的效果,降低了通信功耗。     

延迟优化的片上网络低功耗映射*

片上网络（NoC）是解决传统基于总线的片上系统（SoC）所面临的功耗、延迟、同步和信号完整性等挑战的有效解决方案。功耗和延迟是NoC设计中的重要约束和性能指标,在设计的各个阶段都存在着优化空间。基于蚁群优化算法,通过通信链路上并发通信事件的均匀分布来降低NoC映射阶段的功耗和延迟。仿真实验表明,与链路通信量负载均衡的方法相比,该方案能进一步在拓扑映射阶段优化功耗和延迟。     

基于电压岛的能量和可靠性感知NoC映射

面向支持电压岛的NoC平台,定义了可靠性约束下的能量感知NoC映射问题,提出一种基于禁忌搜索的优化方法.设计了一种新的能效变化率驱动的启发式算法,嵌套于NoC设计空间的搜索过程中,在IP核映射解的基础上实现各电压岛的电压映射.实验结果表明,本文算法可显著降低NoC能耗,并高效地确保NoC通信的可靠性要求.     

面向时延的NoC映射技术研究

针对NoC任务映射问题中时延难以预测和启发式算法效率低的问题,提出一个时延改进模型和近邻随机遗传算法。该模型从宏观的链路负载分布和单个节点的排队时延两方面来构建NoC映射的时延模型,通过引入时延因子、权重系数来刻画不同映射方案对时延性能的影响,避免了NoC通信时延精确建模的难题。提出近邻随机思想来构建遗传算法的初始种群,并且运用该算法实现了面向时延的NoC映射,在达到全局最优的情况下,比经典遗传算法效率提升将近20%。实验结果表明,该算法优于现有的经典遗传算法和随机映射方案。     

LPNet: A DNN based latency prediction technique for application mapping in Network-on-Chip design

Analytical models used for latency estimation of Network-on-Chip (NoC) are not producing reliable accuracy. This makes these analytical models difficult to use in optimization of design space exploration. In this paper, we propose a learning based model using deep neural network (DNN) for latency predictions. Input features for DNN model are collected from analytical model as well as from Booksim simulator. Then this DNN model has been adopted in mapping optimization loop for predicting the best mapping of given application and NoC parameters combination. Our simulations show that using the proposed DNN model, prediction error is less than 12% for both synthetic and application specific traffic. More than 108 times speedup could be achieved using DPSO with DNN model compared to DPSO using Booksim simulator.     

基于遗传蚁群算法的片上网络映射研究

基于2D Mesh结构的片上网络在设计之初就要考虑模块映射问题,以满足通信功耗的约束。提出一种基于遗传蚁群映射算法的方法解决片上网络设计中通信功耗最小化问题。该算法针对标准蚁群算法易于出现早熟停滞等缺陷,引入轮盘赌选择机制及染色体杂交等手段,使映射功耗函数快速收敛,达到良好的全局寻优效果。     

Characterizing the impact of process variation on 45 nm NoC-based CMPs

Current integration scales make possible to design chip multiprocessors with a large amount of cores interconnected by a NoC. Unfortunately, they also bring process variation, posing a new burden to processor manufacturers.Regarding the NoC, variability causes that the delays of links and routers do not match those initially established at design time. In this paper we analyze how variability affects the NoC by applying a new variability model to 100 instances of an 8 × 8 mesh NoC synthesized using 45 nm technology. We also show that GALS-based NoCs present communication bottlenecks due to the slower components of the network, which cause congestion, thus reducing performance. This performance reduction finally affects the applications being executed in the CMP because they may be mapped to slower areas of the chip. In this paper we show that using a mapping algorithm that considers variability data may improve application execution time up to 50%.     

拓扑结构感知的片上网络体系结构应用映射与优化

应用映射是片上网络体系结构研究的关键问题之一,映射结果的好坏会极大地影响体系结构的性能。现有的应用映射方法大多基于特定的网络结构,如2d-mesh、2d-torus等,研究NoC性能或功耗约束的应用映射与优化方法。本文提出了一种拓扑结构感知的基于高层代码转换的片上网络应用映射与优化方法。该方法采用多面体模型对应用的核心循环进行自动并行和局部性优化,并将网络拓扑结构抽象成带权重的有向图,使用该有向图对任务流图进行覆盖,以提高任务的并行性,降低任务间同步和通信开销。实验结果表明,采用优化的映射方法后任务节点间的并行性被充分利用,通信开销降低,整体上提高了片上网络系统性能。     

Quantifying the impact of uncertainty in embedded systems mapping for NoC based architectures

We describe a modeling framework to capture and account for uncertainty in design parameters in embedded systems. We then develop an uncertainty-aware solution to the problem of mapping in embedded systems that uses Network-on-Chip (NoC) based architecture platforms. The problem of mapping is formulated as a multi-objective - reliability, performance, and energy consumption - optimization problem. To solve this problem, we propose a solution based on the NSGA-II genetic algorithm and Monte Carlo simulation techniques. The solution is implemented as a computer-aid design tool that can generate robust 3D Pareto frontiers in the solution space formed by the design objectives of reliability, performance, and energy consumption. Comparison to several state-of-the-art models and solutions for the mapping problem, indicate that significant differences in the actual values of the design attribute of interest exist when one considers uncertainty in design parameters. For example, in the case of mapping with reliability as the only objective, 10% uncertainty in design parameters can lead to a 10.06% difference in MTTF estimation. In the case of mapping with execution time and energy consumption as objectives, the difference in 2D Pareto frontiers due to 10% uncertainty in design parameters can be up to 7.9%. These differences are important because they can mislead the overall optimization process of mapping toward suboptimal solution points. The DESUU-NOC tool that implements the proposed multi-objective mapping algorithm has as a main feature and contribution of this paper the ability to generate 3D Pareto frontiers comprised of robust solution points.