We present a demonstration of image classification using an echo-state network (ESN) relying on a single simulated spintronic nanostructure known as the vortex-based spin-torque oscillator (STVO) delayed in time. We employ an ultrafast data-driven simulation framework called the data-driven Thiele equation approach (DD-TEA) to simulate the STVO dynamics. This allows us to avoid the challenges associated with repeated experimental manipulation of such a nanostructured system. We showcase the versatility of our solution by successfully applying it to solve classification challenges with the MNIST, EMNIST-letters and Fashion MNIST datasets. Through our simulations, we determine that within an ESN with numerous learnable parameters the results obtained using the STVO dynamics as an activation function are comparable to the ones obtained with other conventional nonlinear activation functions like the reLU and the sigmoid. While achieving state-of-the-art accuracy levels on the MNIST dataset, our model's performance on EMNIST-letters and Fashion MNIST is lower due to the relative simplicity of the system architecture and the increased complexity of the tasks. We expect that the DD-TEA framework will enable the exploration of deeper architectures, ultimately leading to improved classification accuracy.

翻译：我们展示了一种基于单个自旋电子纳米结构——即时延涡旋基自旋扭矩振荡器（STVO）——的储备池计算网络（ESN）在图像分类中的应用演示。我们采用名为数据驱动Thiele方程方法（DD-TEA）的超快数据驱动仿真框架来模拟STVO动力学，从而避免了重复实验操控此类纳米结构系统所带来的挑战。通过成功将该方法应用于MNIST、EMNIST-letters和Fashion MNIST数据集的分类问题，我们验证了方案的通用性。仿真结果表明，在包含大量可学习参数的ESN中，使用STVO动力学作为激活函数所得结果与使用ReLU和sigmoid等传统非线性激活函数的结果相当。尽管模型在MNIST数据集上达到了当前最先进的分类精度，但由于系统架构相对简单且任务复杂度增加，其在EMNIST-letters和Fashion MNIST数据集上的表现有所下降。我们预期DD-TEA框架将推动更深度架构的探索，最终实现分类精度的进一步提升。