利用神经启发的AI加速器在6G网络中实现高速计算
Chunxiao Lin1, Muhammad Farhan Azmine1, Yibin Liang1
1Bradley Department of Electrical and Computing Engineering, Virginia Tech, Blacksburg, VA, United States.
Frontiers in computational neuroscience
|March 7, 2024
概括
本研究介绍了一种神经科学启发的机器学习模型,即回声状态网络 (ESN),用于在6G无线通信系统中更快地检测符号. 硬件加速设计显示了大规模MIMO-OFDM网络的高性能和效率.
科学领域:
- 无线通信无线通信
- 机器学习 机器学习
- 神经科学启发的计算
背景情况:
- 6G技术要求更高的数据速率和处理速度.
- 能源效率对于实际实施6G至关重要.
- 大规模的MIMO-OFDM系统是6G的关键支持者.
研究的目的:
- 在大规模的MIMO-OFDM系统中应用回声状态网络 (ESN) 进行高效的符号检测.
- 设计和验证用于符号检测的硬件加速ESN架构.
- 在现实场景中评估基于ESN的系统的性能和可行性.
主要方法:
- 利用一种神经科学启发的机器学习模型:回声状态网络 (ESN).
- 开发了一个硬件加速的储存神经元架构,用于ESN实现.
- 在Xilinx Virtex-7 FPGA板上验证了设计.
主要成果:
- 与传统方法 (如线性MMSE) 相比,基于ESN的符号探测器表现出卓越的性能和可扩展性.
- 实现了低位错误率,这表明检测准确度很高.
- 在FPGA上展示了低资源利用率和高吞吐量.
结论:
- 拟议的硬件加速ESN是6G大规模MIMO-OFDM系统中符号检测的可行和高性能解决方案.
- 由神经科学启发的模型为推进无线通信技术提供了一个有希望的方法.
- 系统设计验证了ESN在苛刻的通信环境中的实际适用性.
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