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纳米级、高能效的人工神经元用于神经形态计算提供了一条前景广阔的途径。先前研究表明，利用反铁磁振荡器可以制造出超快脉冲人工神经元，这类神经元能够模拟生物神经元的诸多独特特性。本研究中，我们训练了一个由反铁磁神经元构成的人工神经网络以执行模式识别任务。训练过程中采用了一种名为脉冲模式关联神经元的简易机器学习算法，该算法依赖于神经元脉冲的时间位置。在不足一微秒的物理时间内，该反铁磁神经网络通过在规定时间窗口内产生脉冲，成功实现了对网格构成符号的识别。我们通过增加输出层来抑制非期望脉冲，进一步实现了多符号识别功能。通过结合反铁磁神经元与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模式识别。