Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, Korea.
Semiconductor R&D Center, Samsung Electronics Co. Ltd, Hwasung, 18448, Korea.
Nat Commun. 2020 Aug 7;11(1):3936. doi: 10.1038/s41467-020-17849-3.
Brain-inspired parallel computing, which is typically performed using a hardware neural-network platform consisting of numerous artificial synapses, is a promising technology for effectively handling large amounts of informational data. However, the reported nonlinear and asymmetric conductance-update characteristics of artificial synapses prevent a hardware neural-network from delivering the same high-level training and inference accuracies as those delivered by a software neural-network. Here, we developed an artificial van-der-Waals hybrid synapse that features linear and symmetric conductance-update characteristics. Tungsten diselenide and molybdenum disulfide channels were used selectively to potentiate and depress conductance. Subsequently, via training and inference simulation, we demonstrated the feasibility of our hybrid synapse toward a hardware neural-network and also delivered high recognition rates that were comparable to those delivered using a software neural-network. This simulation involving the use of acoustic patterns was performed with a neural network that was theoretically formed with the characteristics of the hybrid synapses.
脑启发式并行计算通常使用由大量人工突触组成的硬件神经网络平台来执行，是有效处理大量信息数据的一种有前途的技术。然而，报道的人工突触的非线性和非对称电导更新特性阻止了硬件神经网络提供与软件神经网络相同的高级训练和推理精度。在这里，我们开发了一种具有线性和对称电导更新特性的人工范德华混合突触。选择使用二硒化钨和二硫化钼通道来增强和降低电导。随后，通过训练和推理模拟，我们展示了我们的混合突触在硬件神经网络中的可行性，并实现了与使用软件神经网络相当的高识别率。这个使用声图案的模拟是在一个理论上由混合突触的特性形成的神经网络中进行的。
