带有尖端神经网络的听觉感知架构和FPGA上的实现
Bin Deng1, Yanrong Fan1, Jiang Wang1
1School of Electrical and Information Engineering, Tianjin University, China.
概括
这项研究介绍了一个强大的,生物启发的尖端神经网络 (SNN) 感知. 开发的系统在听觉任务中表现出高性能和卓越的噪声稳定性,推进了神经形态工程.
科学领域:
- 神经形态工程的神经形态工程
- 生物启发的计算技术
- 人工感知是一种人工感知.
背景情况:
- 尖端神经网络 (SNN) 提供了一种新的感知方法,灵感来自生物系统.
- 开发高性能,灵活的SNN架构对于推进神经形态计算至关重要.
- 现有的SNN系统经常面临强大的感知挑战,特别是在杂的环境中.
研究的目的:
- 展示一种以生物为灵感的,基于尖峰的SNN感知数字系统,以实现强大的感知.
- 为了提高特征提取处理速度和整体系统性能.
- 为了证明系统在听觉感知任务中的有效性.
主要方法:
- 实施一个完全并行的管道方案,用于加速的特征提取.
- 使用十个Intel Cyclone现场可编程网关阵列 (FPGA) 实现一个听觉感知系统原型.
- 测试语音识别精度使用TIMIT数字语音在各种信号与噪声比率 (SNRs) 的噪声中.
主要成果:
- 该系统实现了107.28 MHz的最大运行频率和5364 Mbps的吞吐量.
- 证明了低功耗 (5.148W/系统) 和高能效 (845.85μJ).
- 在噪音条件下 (20dB SNR) 达到高达85.75%的语音识别精度,随着SNR的下降,精度降低最小.
结论:
- 拟议的基于尖峰的SNN感知系统提供了强大而高性能的听觉感知能力.
- 灵活的,生物灵感的架构代表了神经形态计算系统的重大进步.
- 该系统的性能优于最先进的SNN感知系统,特别是在具有挑战性的杂环境中.
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