超低功率电子模拟生物Fitzhugh-Nagumo神经元
Ragib Ahsan1, Zezhi Wu1, Seyedeh Atiyeh Abbasi Jalal1
1Department of Electrical and Computer Engineering, University of Southern California, Los Angeles 90089-0001, United States.
ACS omega
|April 29, 2024
概括
研究人员使用道二极管和MOSFET开发了一种模仿生物神经元的新型电子电路. 这种超低功率电路可以实现高效的尖端神经网络并解决复杂的问题,在神经形态计算硬件方面取得了突破.
科学领域:
- 电子工程 电子工程
- 计算神经科学是一种神经科学.
- 材料科学 材料科学 材料科学
背景情况:
- 生物神经元产生动作潜力 (尖峰) 作为对刺激的反应.
- 通过电子方式模仿神经元功能是先进人工智能硬件的关键.
- 现有的神经形态电路经常面临功率效率和可扩展性方面的挑战.
研究的目的:
- 引入一种新的电子电路,复制基于Fitzhugh-Nagumo (FN) 模型的生物尖端神经元的行为.
- 为了使超低功率振荡和尖端神经网络硬件.
- 为了展示这些人工神经元在解决计算难题中的应用.
主要方法:
- 设计了一个使用道二极管用于负差电阻 (NDR) 和MOSFET用于活性电感的电子电路.
- 模拟和模拟电路的功能,以预测能源消耗和性能.
- 将多个FN神经元集成到一个合的振荡器网络中,形成一个振荡器Ising机器 (OIM).
主要成果:
- FN神经元电路成功地将直流电压激发转化为类似于生物动作潜力的电压峰值.
- 预计能源成本为2 aJ/周期,证明了超低功率的运行.
- 该OIM成功地解决了NP完全的最大切割问题,显示了对工艺变化的稳定性.
结论:
- 开发的FN神经元电路是CMOS兼容的,适合超低功率的神经形态硬件.
- 使用结合的FN神经元的基于振荡器的计算为解决复杂的计算任务提供了有希望的方法.
- 这项工作为节能和强大的人工智能系统铺平了道路.
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