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Dedicated hardware implementations of spiking neural networks that combinethe advantages of mixed-signal neuromorphic circuits with those of emergingmemory technologies have the potential of enabling ultra-low power pervasivesensory processing. To endow these systems with additional flexibility and theability to learn to solve specific tasks, it is important to developappropriate on-chip learning mechanisms.Recently, a new class of three-factorspike-based learning rules have been proposed that can solve the temporalcredit assignment problem and approximate the error back-propagation algorithmon complex tasks. However, the efficient implementation of these rules onhybrid CMOS/memristive architectures is still an open challenge. Here wepresent a new neuromorphic building block,called PCM-trace, which exploits thedrift behavior of phase-change materials to implement long lasting eligibilitytraces, a critical ingredient of three-factor learning rules. We demonstratehow the proposed approach improves the area efficiency by >10X compared toexisting solutions and demonstrates a techno-logically plausible learningalgorithm supported by experimental data from device measurements

PCM-trace: Scalable Synaptic Eligibility Traces with Resistivity Drift of Phase-Change Materials