本文關(guān)鍵詞：基于量子粒群的三維片上網(wǎng)絡(luò)低功耗映射算法研究　出處：《天津工業(yè)大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 三維片上網(wǎng)絡(luò) 低功耗映射 量子粒子群算法 多樣性控制量子粒子群

【摘要】：二維片上網(wǎng)絡(luò)(2D NoC)是為了克服基于總線系統(tǒng)的芯片(SoC)體系結(jié)構(gòu)在功耗、通信帶寬以及物理設(shè)計(jì)等方面的局限而誕生的。但隨著芯片集成度的進(jìn)一步提高,2D NoC在布局布線、面積、封裝密度以及功耗等方面都已經(jīng)到達(dá)了瓶頸,因而,三維片上網(wǎng)絡(luò)(3D NoC)應(yīng)運(yùn)而生。3D NoC擁有更低的互連損耗、更短的全局互連、更小的體積、更高的封裝密度以及更高的性能等諸多優(yōu)勢(shì)。在3D NoC的研究中,如何將計(jì)算任務(wù)映射到3D NoC節(jié)點(diǎn)上是關(guān)鍵問(wèn)題之一,3DNoC映射問(wèn)題對(duì)系統(tǒng)的功耗、延遲等性能均有很大影響,映射優(yōu)化已成為解決3D NoC降低功耗、改善散熱等問(wèn)題的重要手段,從多個(gè)角度研究更好的3D NoC映射算法非常必要。本文對(duì)3D NoC映射算法進(jìn)行了研究,主要完成了以下工作。首先,利用量子粒子群算法的全局收斂性和收斂速度更快的特點(diǎn),首次將量子粒子群算法應(yīng)用到3D NoC低功耗映射問(wèn)題中,并與基于粒子群的3D NoC映射算法進(jìn)行了對(duì)比,仿真結(jié)果表明,基于量子粒子群的映射算法比基于粒子群的映射算法收斂速度更快,最大提高90.48%；同時(shí)有效地降低了3D NoC的映射功耗,尤其是對(duì)于核數(shù)為20-80的應(yīng)用特征圖優(yōu)化效果更為明顯,功耗最大降低了20.99%；其次,為了解決大規(guī)模3D NoC的低功耗映射問(wèn)題,提出一種基于多樣性控制量子粒子群的低功耗映射算法,并與基于量子粒子群的映射算法進(jìn)行對(duì)比,仿真實(shí)驗(yàn)結(jié)果表明,當(dāng)應(yīng)用特征圖規(guī)模較大(120核以上)時(shí),該算法仍能保持較穩(wěn)定的功耗優(yōu)化效率(4.08%-8.04%),并且收斂速度更快,最大提高了66.7%。
[Abstract]:Two dimensional network on chip (2D NoC) is to overcome the bus system based on chip (SoC) architecture in power, communication bandwidth and physical design of the limitations of the birth. But with the further improvement of chip integration, 2D NoC in the layout, size, package density and power consumption etc have been reached the bottleneck, therefore, three-dimensional network on chip (3D NoC).3D NoC has a lower interconnection loss arises, global interconnect shorter, smaller volume, many advantages of higher packaging density and higher performance. In the research of 3D NoC, how will the computing task is mapped to the 3D node is NoC one of the key issues, power 3DNoC mapping problem of the system, such as delay performance are mapping optimization has become an important means to reduce the power consumption of 3D NoC, improve heat dissipation, from the 3D NoC map of a point of the study is better Method is very necessary. This paper studied the 3D NoC mapping algorithm, mainly completed the following work. Firstly, the characteristics of global convergence and convergence speed of the quantum particle swarm algorithm is faster, the quantum particle swarm algorithm is applied to the 3D NoC low-power mapping problem, and compare with the algorithm of 3D NoC mapping particle based on the population, the simulation results show that the convergence speed based mapping algorithm based on mapping algorithm, quantum particle swarm particle swarm faster, the biggest increase of 90.48%; while effectively reducing the power consumption of the 3D mapping of NoC, especially for the audit for the application of 20-80 feature map optimization effect is more obvious, the maximum power consumption is reduced by 20.99%; secondly, in order to solve the problem of low power mapping large-scale 3D NoC, proposes a control algorithm based on the diversity of low-power mapping quantum particle swarm, and the mapping algorithm based on quantum particle swarm The simulation results show that the algorithm can maintain a more stable power optimization efficiency (4.08%-8.04%) when the application characteristic map is large (120 cores or more), and the convergence speed is faster, and the maximum 66.7%. is increased.

【學(xué)位授予單位】：天津工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TN47

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