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基于遗传算法的电压岛感知的多电压分配
引用本文:杜世民,夏银水,戚利侠.基于遗传算法的电压岛感知的多电压分配[J].浙江大学学报(理学版),2013,40(1):56-61.
作者姓名:杜世民  夏银水  戚利侠
作者单位:1. 宁波大学科学技术学院,浙江宁波,315212
2. 宁波大学信息科学与工程学院,浙江宁波,315212
基金项目:国家自然科学基金重点项目(61131001);国家自然科学基金资助项目(61041001);浙江省教育厅科研计划资助项目(Y200906637);宁波大学学科项目(xkl096)
摘    要:功耗是当前SoC设计所面临的最大挑战之一,多电压设计是一种降低SoC芯片功耗的有效方法.在后布局阶段应用多电压设计,首先对现有电源网络复杂性度量方法进行改进,然后提出了一个同时考虑功耗、电源网络复杂性及电平转换器的新目标函数,并采用遗传算法进行最优电压分配.对GSRC测试电路的实验结果表明,所提出的算法不仅能有效降低芯片功耗,同时可以将多电压设计的额外开销控制在一个较低的水平.此外,改进的电源网络复杂性度量方法在功耗节省和电平转换器数量方面较已有的有一定的优势.

关 键 词:功耗  多电压设计  电压岛  遗传算法  电平转换器
收稿时间:2011-08-07;

Voltage island-aware multiple voltage assignment based on genetic algorithm
DU Shi-min , XIA Yin-shui , QI Li-xia.Voltage island-aware multiple voltage assignment based on genetic algorithm[J].Journal of Zhejiang University(Sciences Edition),2013,40(1):56-61.
Authors:DU Shi-min  XIA Yin-shui  QI Li-xia
Institution:1.College of Science & Technology,Ningbo University,Ningbo 315212,Zhejiang Province,China;2.School of Information Science and Engineering,Ningbo University,Ningbo 315212,Zhejiang Province,China)
Abstract:Power consumption has become one of the most crucial challenges in modern SoC design. Multiple Supply Voltages (MSV) is an effective way to reduce the SoC's power consumption. In this paper, we apply the MSV de sign method in the post-floorplanning stage. At first, we propose an improved method to model the power supply network complexity, then an novel objective function simultaneously considering power consumption, power network complexity and level shifters' number is proposed and the genetic algorithm is exploited to assign the most ap propriate voltage to each module of SoC. Experimental results on GSRC benchmarks show that the proposed algo rithm can effectively reduce the chip power consumption, while controlling the overhead of MSV design at a lower level. In addition, the improved model of power supply network complexity has some advantages in power savings and the reduction of the level shifters' number.
Keywords:power consumption  MSV  voltage island  genetic algorithm  level shifters
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