PCM-trace：具有相变材料电阻率漂移的可缩放突触合格性迹线
PCM-trace: Scalable Synaptic Eligibility Traces with Resistivity Drift of Phase-Change Materials
摘要：
脉冲神经网络的专用硬件实现结合了混合信号神经形态电路与新兴存储器技术的优点，具有实现超低功耗普适感觉处理的潜力。为了赋予这些系统额外的灵活性和学习能力，以解决特定的任务，重要的是开发适当的片上学习mechanism.Recently，一类新的三因素尖峰为基础的学习规则已被提出，可以解决时间信用分配问题和近似的误差反向传播算法在复杂的任务。然而，在混合CMOS/忆阻架构上有效实现这些规则仍然是一个开放的挑战。在这里，我们提出了一种新的神经形态构建块，称为PCM跟踪，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成分。我们展示了所提出的方法如何提高面积效率> 10倍，与现有的解决方案相比，并展示了一个技术逻辑上合理的学习算法支持的实验数据，从设备测量
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
