论文标题
转移和节能生物相容性石墨烯人工突触晶体管，以增强精度神经形态计算
Metaplastic and Energy-Efficient Biocompatible Graphene Artificial Synaptic Transistors for Enhanced Accuracy Neuromorphic Computing
论文作者
论文摘要
在并行数据存储和处理方面，采用von Neumann架构的基于CMOS的计算系统相对有限。相反，人脑是一个活着的计算信号处理单元，以极端的并行性和能源效率运行。尽管在过去的十年中已经出现了许多神经形态电子设备，但其中大多数是刚性或含有对生物系统有毒的材料。在这项工作中，我们报告了基于生物相容性双层石墨烯的人工突触晶体管（BLAST），能够模仿突触行为。爆炸装置利用了干燥离子选择性膜，可实现长期增强，约50 AJ/M^2开关能效效率，至少比以前关于二维基于材料的人工突触的报道低一个数量级。这些设备显示出独特的跨塑性，这是可推广的深神经网络的有用功能，我们证明了Metaplastic Blast在经典图像分类任务中的优先理想线性突触。随着开关能量远低于每生物突触估计的1 FJ能量，所提出的设备是生物互化在线学习的有力候选者，弥合了人工和生物神经网络之间的差距。
CMOS-based computing systems that employ the von Neumann architecture are relatively limited when it comes to parallel data storage and processing. In contrast, the human brain is a living computational signal processing unit that operates with extreme parallelism and energy efficiency. Although numerous neuromorphic electronic devices have emerged in the last decade, most of them are rigid or contain materials that are toxic to biological systems. In this work, we report on biocompatible bilayer graphene-based artificial synaptic transistors (BLAST) capable of mimicking synaptic behavior. The BLAST devices leverage a dry ion-selective membrane, enabling long-term potentiation, with ~50 aJ/m^2 switching energy efficiency, at least an order of magnitude lower than previous reports on two-dimensional material-based artificial synapses. The devices show unique metaplasticity, a useful feature for generalizable deep neural networks, and we demonstrate that metaplastic BLASTs outperform ideal linear synapses in classic image classification tasks. With switching energy well below the 1 fJ energy estimated per biological synapse, the proposed devices are powerful candidates for bio-interfaced online learning, bridging the gap between artificial and biological neural networks.
