Dedicated hardware implementations of spiking neural networks that combine the advantages of mixed-signal neuromorphic circuits with those of emerging memory technologies have the potential of enabling ultra-low power pervasive sensory processing. To endow these systems with additional flexibility and the ability to learn to solve specific tasks, it is important to develop appropriate on-chip learning mechanisms.Recently, a new class of three-factor spike-based learning rules have been proposed that can solve the temporal credit assignment problem and approximate the error back-propagation algorithm on complex tasks. However, the efficient implementation of these rules on hybrid CMOS/memristive architectures is still an open challenge. Here we present a new neuromorphic building block,called PCM-trace, which exploits the drift behavior of phase-change materials to implement long lasting eligibility traces, a critical ingredient of three-factor learning rules. We demonstrate how the proposed approach improves the area efficiency by >10X compared to existing solutions and demonstrates a techno-logically plausible learning algorithm supported by experimental data from device measurements

中文翻译：

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PCM迹线：具有相变材料电阻率漂移的可伸缩突触合格迹线

结合混合信号神经形态电路的优点和新兴存储技术的优点的尖刺神经网络的专用硬件实现，具有实现超低功耗普及型感官处理的潜力。为了赋予这些系统更多的灵活性和学习解决特定任务的能力，开发合适的片上学习机制非常重要。最近，已经提出了一种新的基于三因素峰值的学习规则，可以解决该问题。时间信用分配问题，并对复杂任务的误差反向传播算法进行近似估计。但是，在混合CMOS /忆阻架构上有效实施这些规则仍然是一个挑战。在这里，我们提出了一个新的神经形态构造块，称为PCM迹线，它利用相变材料的漂移行为来实现持久的资格跟踪，这是三因素学习规则的关键组成部分。与现有解决方案相比，我们演示了所提出的方法如何将区域效率提高了10倍以上，并演示了由设备测量的实验数据支持的技术上合理的学习算法