一个基于FPGA的实时元启发式处理器,以有效地模拟PSO算法的新变体
Esteban Anides1, Guillermo Salinas1, Eduardo Pichardo1
1Instituto Politécnico Nacional, ESIME Culhuacan, Av. Santa Ana No. 1000, Ciudad de México 04260, Mexico.
Micromachines
|July 8, 2023
概括
这项研究引入了一种新的马科维亚交换粒子集群优化 (PSO) 算法,以提高声回声取消 (AEC) 性能. 改进的算法可以动态调整群体大小,降低高质量的音频通信的计算成本.
科学领域:
- 信号处理 信号处理
- 人工智能的人工智能
- 硬件加速器 硬件加速器
背景情况:
- 高性能音频通信系统需要卓越的音频质量.
- 使用粒子群优化 (PSO) 的现有声波回声取消器 (AEC) 由于过早的融合而遭受性能恶化.
- 需要改进的AEC算法来保持高性能,同时降低计算复杂性.
研究的目的:
- 提出一种新的PSO算法的变体,以克服AEC的过早趋同.
- 在PSO算法中引入一个动态人口规模调整机制.
- 为高性能AEC系统在FPGA上实施拟议的算法,提出一个并行硬件架构.
主要方法:
- 开发了一种新的PSO变体,采用马科维亚切换技术.
- 在过过程中实施了动态种群大小调整机制.
- 在Stratix IV GX EP4SGX530 FPGA上设计了一个并行元启发处理器,利用时间复杂化进行变量群体模拟.
主要成果:
- 拟议的马科维亚切换PSO算法有效地减轻了过早的趋同.
- 动态人口大小调整大大降低了计算成本.
- 平行硬件架构可以有效地改变群体大小,以提高处理能力.
结论:
- 拟议的算法在声回声取消方面表现出卓越的性能.
- 新的并行硬件架构促进了高性能AEC系统的开发.
- 这种综合方法为先进的音频通信设备提供了有前途的解决方案.
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