三維片上網(wǎng)絡(luò)離散量子粒子群布圖算法研究
發(fā)布時(shí)間：2018-12-18 17:27
【摘要】：三維片上網(wǎng)絡(luò)在多種性能上均優(yōu)于二維片上網(wǎng)絡(luò),已成為研究熱點(diǎn)。布圖算法直接影響芯片的面積和布線長(zhǎng)度,成為三維片上網(wǎng)絡(luò)優(yōu)化設(shè)計(jì)的重要方向。提出一種基于離散粒子群算法的三維片上網(wǎng)絡(luò)布圖優(yōu)化算法,與之前常使用的模擬退火算法相比,不再使用單一解局部擾動(dòng)的方式得到整個(gè)解空間,該算法采用初始化隨機(jī)種群并不斷迭代的進(jìn)化方式,具有更優(yōu)的搜索能力和更快的收斂速度。仿真結(jié)果表明,采用該算法選擇布圖方案可以顯著降低微片延遲,節(jié)省CPU計(jì)算時(shí)間,尤其是在IP核數(shù)量眾多的測(cè)試用例和高注入率情況下效果更為明顯,如對(duì)于ami49測(cè)試用例當(dāng)注入率為100%時(shí),基于離散量子粒子群算法的結(jié)果和基于模擬退火算法的結(jié)果相比,平均微片延遲減少了20.63%,CPU平均時(shí)間減少了69.40%。
[Abstract]:Three-dimensional on-chip network is superior to two-dimensional on-chip network in many performance, so it has become a research hotspot. The layout algorithm directly affects the chip area and wiring length, and becomes an important direction of 3D on-chip network optimization design. In this paper, a new algorithm based on discrete particle swarm optimization (DPSO) is proposed. Compared with the previous simulated annealing algorithm, the entire solution space is obtained by using a single local perturbation method. The algorithm has better searching ability and faster convergence speed by initializing random population and iterating continuously. The simulation results show that the algorithm can significantly reduce the delay of microchip and save the computing time of CPU, especially in the case of large number of test cases and high injection rate of IP cores. For ami49 test cases, when the injection rate is 100, the average microchip delay is reduced by 20.63 and 69.40, compared with the results based on discrete Quantum Particle Swarm Optimization (DQPSO) and simulated annealing (SA).
【作者單位】： 天津工業(yè)大學(xué)計(jì)算機(jī)科學(xué)與軟件學(xué)院;
【基金】：國(guó)家自然科學(xué)基金No.61272006~~
【分類號(hào)】：TP18
本文編號(hào)：2386221
[Abstract]:Three-dimensional on-chip network is superior to two-dimensional on-chip network in many performance, so it has become a research hotspot. The layout algorithm directly affects the chip area and wiring length, and becomes an important direction of 3D on-chip network optimization design. In this paper, a new algorithm based on discrete particle swarm optimization (DPSO) is proposed. Compared with the previous simulated annealing algorithm, the entire solution space is obtained by using a single local perturbation method. The algorithm has better searching ability and faster convergence speed by initializing random population and iterating continuously. The simulation results show that the algorithm can significantly reduce the delay of microchip and save the computing time of CPU, especially in the case of large number of test cases and high injection rate of IP cores. For ami49 test cases, when the injection rate is 100, the average microchip delay is reduced by 20.63 and 69.40, compared with the results based on discrete Quantum Particle Swarm Optimization (DQPSO) and simulated annealing (SA).
【作者單位】： 天津工業(yè)大學(xué)計(jì)算機(jī)科學(xué)與軟件學(xué)院;
【基金】：國(guó)家自然科學(xué)基金No.61272006~~
【分類號(hào)】：TP18
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