An All-in-One Bioinspired Neural Network
An All-in-One Bioinspired Neural Network
复制标题
一体化仿生神经网络
DOI：
10.1021/acsnano.2c02172
复制
发表时间：
2022
期刊：
影响因子：
17.1
通讯作者：
Das, Saptarshi
中科院分区：
文献类型：
--
作者：
Subbulakshmi Radhakrishnan, Shiva;Dodda, Akhil;Das, Saptarshi
尽管人工神经网络(ANN)最近取得了进展，但生物神经网络的能量效率、多功能性、适应性和集成性在很大程度上仍然是硬件神经形态计算系统无法模仿的。在这里，我们利用基于新兴的二维(2D)分层材料(如MoS2)的光电子、计算和可编程存储器件来展示一种单片集成的、多像素的、能够以极小的能量消耗进行感知、编码、学习、遗忘和推断的生物启发神经网络(BNN)。我们还演示了学习适应性，并模拟了特定突触条件下的学习挑战，以模拟生物学习。我们的发现突出了基于新兴的2D材料、器件和集成电路的内存计算和传感的潜力，不仅可以克服传统CMOS设计中冯·诺伊曼计算的瓶颈，而且还有助于消除竞争技术所需的外围组件，如忆阻器。
In spite of recent advancements in artificial neural networks (ANNs), the energy efficiency, multifunctionality, adaptability, and integrated nature of biological neural networks remain largely unimitated by hardware neuromorphic computing systems. Here, we exploit optoelectronic, computing, and programmable memory devices based on emerging two-dimensional (2D) layered materials such as MoS2to demonstrate a monolithically integrated, multipixel, and “all-in-one” bioinspired neural network (BNN) capable of sensing, encoding, learning, forgetting, and inferring at minuscule energy expenditure. We also demonstrate learning adaptability and simulate learning challenges under specific synaptic conditions to mimic biological learning. Our findings highlight the potential of in-memory computing and sensing based on emerging 2D materials, devices, and integrated circuits to not only overcome the bottleneck of von Neumann computing in conventional CMOS designs but also to aid in eliminating the peripheral components necessary for competing technologies such as memristors.