The impressive performance of artificial neural networks has come at the cost of high energy usage and CO$_2$ emissions. Unconventional computing architectures, with magnetic systems as a candidate, have potential as alternative energy-efficient hardware, but, still face challenges, such as stochastic behaviour, in implementation. Here, we present a methodology for exploiting the traditionally detrimental stochastic effects in magnetic domain-wall motion in nanowires. We demonstrate functional binary stochastic synapses alongside a gradient learning rule that allows their training with applicability to a range of stochastic systems. The rule, utilising the mean and variance of the neuronal output distribution, finds a trade-off between synaptic stochasticity and energy efficiency depending on the number of measurements of each synapse. For single measurements, the rule results in binary synapses with minimal stochasticity, sacrificing potential performance for robustness. For multiple measurements, synaptic distributions are broad, approximating better-performing continuous synapses. This observation allows us to choose design principles depending on the desired performance and the device's operational speed and energy cost. We verify performance on physical hardware, showing it is comparable to a standard neural network.

翻译：人工神经网络的卓越性能是以高能耗和二氧化碳排放为代价的。非常规计算架构（以磁性系统为代表）具有成为替代性节能硬件的潜力，但在实际应用中仍面临随机行为等挑战。本文提出了一种利用纳米线中磁性畴壁运动中传统上有害的随机效应的方法。我们展示了功能性二元随机突触，并配以梯度学习规则，该规则可训练此类突触并适用于多种随机系统。该规则利用神经元输出分布的均值和方差，根据对每个突触的测量次数，在突触随机性与能效之间找到平衡。对于单次测量，该规则产生随机性最小的二元突触，以牺牲潜在性能换取鲁棒性；对于多次测量，突触分布较宽，近似于性能更优的连续突触。这一发现使我们能够根据所需性能、器件运行速度和能耗成本选择设计原则。我们在物理硬件上验证了性能，证明其与标准神经网络相当。