音频信号刺激多层HfOx/TiOy尖端神经网络用于神经形态计算
Shengbo Gao1,2, Mingyuan Ma2,3,4, Bin Liang1,2
1School of Physics, Nanjing University, Nanjing 210093, China.
Nanomaterials (Basel, Switzerland)
|September 13, 2024
概括
研究人员为人工神经元和突触开发了新型的memristor设备,使得可有效地训练用于音频信号处理的尖端神经网络 (SNN). 这一突破为具有成本效益的AI芯片提供了新的途径.
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
- 人工智能的人工智能
背景情况:
- 由于其类似神经元的特性,memristors对于使用尖端神经网络 (SNNs) 的类似大脑的芯片至关重要.
- 设计稳定的人工神经元和突触与可控制的SNN硬件切换仍然是一个重大挑战.
研究的目的:
- 报告首次使用多层HfOx/TiOγ记忆器交叉杆阵列用于音频信号的SNN训练.
- 为了研究这些memristor设备中的可调节开关特性及其潜在机制.
主要方法:
- 制造多层HfOx/TiOγ记忆器交叉杆阵列.
- 可调节的挥发性和非挥发性切换行为的表征.
- 使用值切换模拟生物神经元的整合和发射功能.
- 构建一个硬件SNN架构用于音频信号处理.
主要成果:
- 通过原子氧空隙路径控制的多层HfOx/TiOγ记忆器中,证明了可调节的挥发性和非挥发性切换.
- 成功模拟了生物神经元的整合和发射功能.
- 为音频信号处理构建了一个稳定的硬件SNN,使集成和发射功能成为可能.
结论:
- 多层HfOx/TiOγ记忆器为SNN中的人工神经元和突触提供了一个可行的平台.
- 开发的memristor设计为AI芯片集成提供了具有成本效益的方法.
- 这项工作为AI时代的先进神经形态计算范式铺平了道路.
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