Spintronic devices offer a promising avenue for the development of nanoscale, energy-efficient artificial neurons for neuromorphic computing. It has previously been shown that with antiferromagnetic (AFM) oscillators, ultra-fast spiking artificial neurons can be made that mimic many unique features of biological neurons. In this work, we train an artificial neural network of AFM neurons to perform pattern recognition. A simple machine learning algorithm called spike pattern association neuron (SPAN), which relies on the temporal position of neuron spikes, is used during training. In under a microsecond of physical time, the AFM neural network is trained to recognize symbols composed from a grid by producing a spike within a specified time window. We further achieve multi-symbol recognition with the addition of an output layer to suppress undesirable spikes. Through the utilization of AFM neurons and the SPAN algorithm, we create a neural network capable of high-accuracy recognition with overall power consumption on the order of picojoules.

翻译：自旋电子器件为开发用于神经形态计算的纳米级、高能效人工神经元提供了有前景的途径。先前研究表明，利用反铁磁（AFM）振荡器可制造超快脉冲人工神经元，模拟生物神经元的诸多独特特征。本研究通过训练由AFM神经元组成的人工神经网络实现模式识别。训练过程中采用一种名为脉冲模式关联神经元（SPAN）的简单机器学习算法，该算法依赖神经元脉冲的时间位置。在不到一微秒的物理时间内，AFM神经网络通过在特定时间窗口内产生脉冲，被训练识别由网格构成的符号。进一步地，通过添加输出层抑制非期望脉冲，我们实现了多符号识别。利用AFM神经元和SPAN算法，我们构建的神经网络能够以皮焦耳量级的整体功耗实现高精度识别。