Integrated Artificial Neural Network with Trainable Activation Function Enabled by Topological Insulator-Based Spin–Orbit Torque Devices
Puyang Huang, Xinqi Liu, Yue Xin, Yu Gu, Albert Lee, Yifan Zhang, Zhuo Xu, Peng Chen, Yu Zhang, Weijie Deng, Guoqiang Yu, Di Wu, Zhongkai Liu, Qi Yao, Yumeng Yang, Zhifeng Zhu, Xufeng Kou
Abstract
Nonvolatile memristors offer a salient platform for artificial neural network (ANN), yet the integration of different function and algorithm blocks into one hardware system remains challenging. Here we demonstrate the brain-like synaptic (SOT-S) and neuronal (SOT-N) functions in the Bi 2 Te 3 /CrTe 2 heterostructure-based spin–orbit torque (SOT) device. The SOT-S unit exhibits highly linear and symmetrical long-term potentiation/depression process, resulting in a fast-training of the MNIST data set with the classification accuracy above 90%. Meanwhile, the Sigmoid-shape transition curve inherited in the SOT-N cell replaces the software-based activation function block, hence reducing the system complexity. On this basis, we employ a serial-connected, voltage-mode sensing ANN architecture to enhance the vector-matrix multiplication signal strength with low reading error of 0.61% while simplifying the peripheral circuitry. Furthermore, the trainable activation function of SOT-N enables the implementation of the Batch Normalization algorithm and activation operation within one clock cycle, which bring about improved on/off-chip training performance close to the ideal baseline.