论文标题
基于抗铁磁自动振荡器的人工神经元作为神经形态计算的平台
Artificial neurons based on antiferromagnetic auto-oscillators as a platform for neuromorphic computing
论文作者
论文摘要
尖峰人造神经元模仿了生物神经元的电压尖峰，并构成了新的一类新的节能，神经形态计算系统的基础。从理论上讲，抗铁磁材料可用于构建尖峰人造神经元。当配置为神经元时，抗铁磁材料中的磁化具有有效的惯性，与常规的人工尖峰神经元相反，它们具有与生物神经元相似的固有特征。这里表明，抗铁磁神经元在皮秒阶的顺序上具有峰值持续时间，每个突触操作的功耗约为10^-3 pj，并且内置特征直接类似于生物神经元，包括响应潜伏期，折射和抑制。还证明，即使对于被动对称的互连，互连与物理神经网络相互连接的抗磁性神经元也可以执行单向数据处理。通过简单的神经形态电路实现布尔逻辑门和可控的记忆环的模拟来说明抗铁磁神经元的灵活性。
Spiking artificial neurons emulate the voltage spikes of biological neurons, and constitute the building blocks of a new class of energy efficient, neuromorphic computing systems. Antiferromagnetic materials can, in theory, be used to construct spiking artificial neurons. When configured as a neuron, the magnetizations in antiferromagnetic materials have an effective inertia that gives them intrinsic characteristics that closely resemble biological neurons, in contrast with conventional artificial spiking neurons. It is shown here that antiferromagnetic neurons have a spike duration on the order of a picosecond, a power consumption of about 10^-3 pJ per synaptic operation, and built-in features that directly resemble biological neurons, including response latency, refraction, and inhibition. It is also demonstrated that antiferromagnetic neurons interconnected into physical neural networks can perform unidirectional data processing even for passive symmetrical interconnects. Flexibility of antiferromagnetic neurons is illustrated by simulations of simple neuromorphic circuits realizing Boolean logic gates and controllable memory loops.
