Parallel weight update protocol for a carbon nanotube synaptic transistor array for accelerating neuromorphic computing.,Nanoscale

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Parallel weight update protocol for a carbon nanotube synaptic transistor array for accelerating neuromorphic computing.
Nanoscale ( IF 5.8 ) Pub Date : 2020-01-08 , DOI: 10.1039/c9nr08979a
Sungho Kim ₁ , Yongwoo Lee , Hee-Dong Kim , Sung-Jin Choi

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Brain-inspired neuromorphic computing has the potential to overcome the inherent inefficiency of the conventional von Neumann architecture by using the massively parallel processing power of artificial neural networks. Neuromorphic parallel processing can be implemented naturally using the crossbar geometry of synaptic device arrays with Ohm's and Kirchhoff's laws. However, selective and parallel weight updates of the synaptic crossbar array are still very challenging due to the unavoidable crosstalk between adjacent devices and sneak path currents. Here, we experimentally demonstrate a weight update protocol in a carbon nanotube synaptic transistor array, where selective and parallel weight updates can be executed by exploiting the individually controllable three terminals of the synaptic device via a localized carrier trapping mechanism. The trained 9 × 8 synaptic array solves four different convolution operations simultaneously for the feature extraction of an image. The massive parallelism and robustness of the weight update protocol are important features toward effective manipulation of big data through neuromorphic computing systems.

中文翻译：

碳纳米管突触晶体管阵列的并行权重更新协议，用于加速神经形态计算。

通过使用人工神经网络的大规模并行处理能力，以脑为灵感的神经形态计算技术有可能克服传统冯·诺依曼架构固有的效率低下的问题。神经形态并行处理可以自然地使用符合欧姆定律和基尔霍夫定律的突触设备阵列的交叉几何来实现。但是，由于相邻设备和潜行电流之间不可避免的串扰，突触交叉开关阵列的选择性和并行权重更新仍然非常具有挑战性。在这里，我们通过实验演示了碳纳米管突触晶体管阵列中的重量更新协议，其中可以通过局部载流子捕获机制利用突触设备的可单独控制的三个端子来执行选择性和并行的重量更新。经过训练的9×8突触阵列可同时解决四种不同的卷积运算，以提取图像的特征。权重更新协议的大规模并行性和鲁棒性是通过神经形态计算系统有效处理大数据的重要特征。

更新日期：2020-01-15

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