Edge devices face challenges when implementing deep neural networks due to constraints on their computational resources and power consumption. Fuzzy logic systems can potentially provide more efficient edge implementations due to their compactness and capacity to manage uncertain data. However, their hardware realization remains difficult, primarily because implementing reconfigurable membership function generators using conventional technologies requires high circuit complexity and power consumption. Here we report a multigate van der Waals interfacial junction transistor based on a molybdenum disulfide/graphene heterostructure that can generate tunable Gaussian-like and π-shaped membership functions. By integrating these generators with peripheral circuits, we create a reconfigurable fuzzy controller hardware capable of nonlinear system control. This fuzzy logic system can also be integrated with a few-layer convolution neural network to form a fuzzy neural network with enhanced performance in image segmentation.

由于计算资源和功耗的限制，边缘设备在实现深度神经网络时面临挑战。模糊逻辑系统由于其紧凑性和管理不确定数据的能力，有可能提供更高效的边缘实现。然而，它们的硬件实现仍然很困难，主要是因为使用传统技术实现可重构成员函数发生器需要高电路复杂性和功耗。在这里，我们报道了一种基于二硫化钼/石墨烯异质结构的多栅范德华界面结晶体管，它可以产生可调谐的类高斯和π形隶属函数。通过将这些发生器与外围电路集成，我们创建了一个能够进行非线性系统控制的可重构模糊控制器硬件。该模糊逻辑系统还可以与几层卷积神经网络集成，形成一个在图像分割方面具有增强性能的模糊神经网络。