在可重新配置的FPGA板上实现FHN神经元模型的高匹配和低成本实现
IEEE transactions on biomedical circuits and systems
|November 29, 2023
概括
研究人员为神经形态工程开发了一种新的混合碎片式基础-2模型 (HPWBM). 这种高效的硬件解决方案精确地复制神经元行为,使用最小的资源和高频率.
科学领域:
- 神经形态工程的神经形态工程
- 计算神经科学是一种神经科学.
- 硬件实现 硬件实现
背景情况:
- 神经形态工程旨在在硬件中复制人类大脑功能.
- 现有的模型往往需要大量的计算资源.
- 准确和高效的神经元建模对于大规模的神经网络至关重要.
研究的目的:
- 为硬件实现引入修改后的Fitz-Hugh Nagumo (FHN) 神经元模型.
- 开发一个计算效率高,准确的神经元模型.
- 为神经形态系统展示一个可扩展的硬件解决方案.
主要方法:
- 修改了Fitz-Hugh Nagumo (FHN) 神经元模型的非线性术语,以创建混合碎片化基-2模型 (HPWBM).
- 使用时间域和相位肖像来验证HPWBM的准确性与原始FHN模型相比.
- 在FPGA上使用硬件描述语言 (HDL) 实现HPWBM,与资源共享和管道.
主要成果:
- 该HPWBM准确地复制FHN模型属性,具有高匹配精度和低计算误差.
- 在Virtex 4 FPGA上的硬件实现只使用了0.48%的资源.
- 该电路实现了448.236 MHz的最大工作频率.
结论:
- 该HPWBM为神经形态应用提供了一个准确且资源高效的模型.
- 拟议的数字硬件方法可以实现大规模的神经元实现.
- 这项工作为大脑启发的计算提供了一个可行的硬件解决方案.
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