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

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 > 10× compared to existing solutions and demonstrates a technologically plausible learning algorithm supported by experimental data from device measurements.