论文标题
由基于拓扑绝缘体的旋转轨道扭矩设备启用了具有可训练激活功能的集成人工神经网络
Integrated Artificial Neural Network with Trainable Activation Function Enabled by Topological Insulator-based Spin-Orbit Torque Devices
论文作者
论文摘要
非挥发性备忘录为人工神经网络（ANN）提供了一个显着的平台，但是将不同功能块集成到一个硬件系统中仍然具有挑战性。在这里，我们证明了基于BI2TE3/CRTE2异质结构的旋转轨道扭矩（SOT）设备中脑样突触（SOT-S）和神经元（SOT-N）功能的实现。 SOT-S单元表现出高度线性（线性误差<4.19％）和对称的长期增强/抑郁过程，与其他备忘录突触相比，性能更好。同时，SOT-N细胞中遗传的Sigmoid-Shape Transition曲线取代了基于软件的激活功能块，从而降低了系统的复杂性。在此基础上，我们采用串行连接的电压模式传感ANN体系结构来增强矢量 - 矩阵乘法信号强度，低读数误差为0.61％。此外，SOT-N的可训练激活函数使集成的SOT-ANN能够在一个时钟周期内执行批准算法和激活操作，从而在接近理想基线的接近/外芯片训练性能中得到改善。
Non-volatile memristors offer a salient platform for artificial neural network (ANN), but the integration of different function blocks into one hardware system remains challenging. Here we demonstrate the implementation of brain-like synaptic (SOT-S) and neuronal (SOT-N) functions in the Bi2Te3/CrTe2 heterostructure-based spin-orbit torque (SOT) device. The SOT-S unit exhibits highly linear (linearity error < 4.19%) and symmetrical long-term potentiation/depression process, resulting in better performance compared to other memristor synapses. 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%. Furthermore, the trainable activation function of SOT-N enables the integrated SOT-ANN to execute 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.
