莫特基于memristor的随机神经元用于概率计算
Aabid Amin Fida1, Sparsh Mittal1, Farooq Ahmad Khanday2
1Electronics and Communication Engineering, Indian Institute of Technology, Roorkee, Uttrakhand, India.
Nanotechnology
|April 9, 2024
概括
这项研究介绍了一种使用memristor技术的新型随机神经元,用于节能的神经形态计算. 开发的尖端神经网络展示了有效的概率学习和推理能力.
科学领域:
- 神经形态工程的神经形态工程
- 材料科学 材料科学 材料科学
- 计算神经科学是一种神经科学.
背景情况:
- 在生物系统中,概率的增加增强了学习和贝叶斯推理.
- 纳米尺度设备中的随机性为神经形态系统提供了潜在的好处.
研究的目的:
- 利用Mott的memristor动力学开发一个随机泄漏的整合和激发 (LIF) 神经元.
- 为了证明神经元对生物神经动力学和概率计算的能力.
- 将神经元集成到高级神经网络架构中,用于学习和推断任务.
主要方法:
- 一个随机LIF神经元的制造,其中包含一个Mott的memristor.
- 将神经元集成到一个编码为人口的尖端神经网络和一个尖端受限制的博尔茨曼机器 (sRBM).
- 评估sRBM对概率学习和推理的准确性.
主要成果:
- 发育的LIF神经元表现出生物神经动态.
- 集成的sRBM实现了87.13%的高精度,与软件实现相美.
- 该设计消除了对外部噪声源的需求,与基于CMOS的概率神经元不同.
结论:
- 基于Mott的memristor的随机LIF神经元可以实现节能和紧的神经形态系统.
- 拟议的神经元有效地在尖端神经网络中实施概率学习和推理.
- 这种方法为先进的低功耗神经形态计算应用提供了有前途的途径.
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