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

Abstract

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 phasechange 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.

Sparse Spiking Gradient Descent

“…Efforts to include training within the neuromorphic system usually rely on local learning rules which do not guarantee state-of-the-art performance. Thus, training on these systems requires error backpropagation on von-Neumann processors instead of taking advantage of the physics of the neuromorphic substrate [49,50].…”