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基于复杂指数的生物启发神经元模型在FPGA中使用Xilinx系统生成器和Vivado设计套件实现.
Maruf Ahmad1, Lei Zhang1, Kelvin Tsun Wai Ng1
1Faculty of Engineering and Applied Science, University of Regina, Regina, SK S4S 0A2, Canada.
Biomimetics (Basel, Switzerland)
|December 22, 2023
概括
在FPGA上的复杂指数神经元为人工神经网络中的电源和延迟问题提供了解决方案. 维瓦多的BRAM实现为生物启发的尖端神经网络提供了高效的硬件.
科学领域:
- 计算机工程 计算机工程
- 人工智能的人工智能
- 神经科学是一个神经科学.
背景情况:
- 传统的人工神经网络面临着计算限制,导致高功耗和延迟,特别是大数据.
- 基于复杂指数的神经元通过简化计算提供了一个计算效率高的替代方案.
- 尖端神经网络 (SNN) 是生物启发的模型,可以潜在地克服这些局限性.
研究的目的:
- 为了研究基于复杂指数的神经元在现场可编程门数组 (FPGA) 上的硬件实现.
- 评估这些实施的性能,准确性和资源效率.
- 展示一种开发高效生物灵感SNNs的途径.
主要方法:
- 使用Xilinx系统生成器和Vivado设计套件实现双神经元和多神经元模型.
- 使用8位,16位和32位的固定点数据格式.
- 对准确性,运行频率,功耗和资源利用 (LUT,FF,BRAM) 的评估.
主要成果:
- 与Simulink相比,基于Vivado BRAM的设计显示出更高的速度,功率效率和资源利用率.
- 拟议的神经元模型在不同的FPGA实现中实现了高精度.
- 维瓦多的BRAM方法成功地支持了多达128个神经元,并实现了最佳的资源使用.
结论:
- 复杂指数神经元是FPGA实施SNN的可行和高效方法.
- 基于Vivado BRAM的设计为SNN的硬件加速提供了显著的优势.
- 这项研究为设计节能和高性能生物启发计算系统提供了基础.
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