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：自旋电子学器件为开发用于神经形态计算的纳米级、节能人工神经元提供了有前景的途径。先前研究已表明，利用反铁磁振荡器可制造出超快尖峰人工神经元，其能模拟生物神经元的许多独特特征。本研究训练了一种由反铁磁神经元组成的人工神经网络以实现模式识别。训练过程中采用了一种名为尖峰模式关联神经元（SPAN）的简单机器学习算法，该算法依赖于神经元尖峰的时间位置。在不到一微秒的物理时间内，反铁磁神经网络通过在规定时间窗口内产生尖峰，被训练识别由网格构成的符号。我们进一步通过添加输出层抑制不期望的尖峰，实现了多符号识别。通过利用反铁磁神经元和SPAN算法，我们构建了一个能够实现高精度识别且总功耗在皮焦耳量级的神经网络。