Demonstration of neuromorphic sequence learning on a memristive array

Abstract

Sequence learning and prediction are considered principle computations performed by biological brains. Machine learning algorithms solve this type of task, but they require large amounts of training data and a substantial energy budget. An approach to overcome these issues and enable sequence learning with brain-like performance is neuromorphic hardware with brain-inspired learning algorithms. The Hierarchical Temporal Memory (HTM) is an algorithm inspired by the working principles of the neocortex and is able to learn and predict continuous sequences of elements. In a previous study, we showed that memristive devices, an emerging non-volatile memory technology, that is considered for energy efficient neuromorphic hardware, can be used as synapses in a biologically plausible version of the temporal memory algorithm of the HTM model. We subsequently presented a simulation study of an analog-mixed signal memristive hardware architecture that can implement the temporal learning algorithm. This architecture, which we refer to as MemSpikingTM, is based on a memristive crossbar array and a control circuitry implementing the neurons and the learning mechanism. In the study presented here, we demonstrate the functionality of the MemSpikingTM algorithm on a real memristive crossbar array, taped out in a commercially available 130nm CMOS technology node co-integrated with HfO based memristive devices. We explain the algorithm and the functionality of the crossbar array and peripheral circuitry and finally demonstrate context-dependent sequence learning using high-order sequences.