In terms of energy efficiency and computational speed, neuromorphic electronics based on non-volatile memory devices is expected to be one of most promising hardware candidates for future artificial intelligence (AI). However, catastrophic forgetting, networks rapidly overwriting previously learned weights when learning new tasks, remains as a pivotal hurdle in either digital or analog AI chips for unleashing the true power of brain-like computing. To address catastrophic forgetting in the context of online memory storage, a complex synapse model (the Benna-Fusi model) has been proposed recently[1], whose synaptic weight and internal variables evolve following a diffusion dynamics. In this work, by designing a proton transistor with a series of charge-diffusion-controlled storage components, we have experimentally realized the Benna-Fusi artificial complex synapse. The memory consolidation from coupled storage components is revealed by both numerical simulations and experimental observations. Different memory timescales for the complex synapse are engineered by the diffusion length of charge carriers, the capacity and number of coupled storage components. The advantage of the demonstrated complex synapse in both memory capacity and memory consolidation is revealed by neural network simulations of face familiarity detection. Our experimental realization of the complex synapse suggests a promising approach to enhance memory capacity and to enable continual learning.

翻译：就能效和计算速度而言，基于非易失性存储器件的神经形态电子学有望成为未来人工智能最具前景的硬件候选方案之一。然而，灾难性遗忘——网络在学习新任务时快速覆盖先前习得的权重——仍是数字或模拟AI芯片释放类脑计算真正潜力的关键障碍。为应对在线存储场景中的灾难性遗忘问题，近期提出了复杂突触模型（Benna-Fusi模型），其突触权重与内部变量遵循扩散动力学演化。在本工作中，通过设计具有一系列电荷扩散控制存储组件的质子晶体管，我们实验实现了Benna-Fusi人工复杂突触。数值模拟与实验观测均揭示了耦合存储组件间的记忆巩固机制。通过调控载流子扩散长度、耦合存储组件的容量及数量，我们设计了复杂突触的多重记忆时间尺度。基于人脸熟悉度检测的神经网络仿真表明，该突触在记忆容量与记忆巩固方面具有显著优势。本工作中复杂突触的实验实现，为增强记忆容量并实现持续学习提供了可行路径。