Neuromorphic System with Phase-Change Synapses for Pattern Learning and Feature Extraction

Neuromorphic systems provide biologically inspired methods of computing, alternative to the classical von Neumann approach. In these systems, computation is performed by a network of spiking neurons controlled by the values of their synaptic weights, which are updated in the process of learning. Providing efficient synaptic learning rules, such as spike-timing-dependent plasticity (STDP), is a challenging task. These rules need to primarily use local information, but simultaneously develop a knowledge representation that is useful in the global context. From the implementation viewpoint, they also need to be suited for particular hardware technology. In this work, we propose a system with spiking neurons and synapses realized using phase-change devices. We design in a bottom-up manner an architecture for pattern learning and feature extraction. Experimental results from a prototype hardware platform demonstrate the capabilities of the proposed neuromorphic system.

Presented at:
2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, Alaska, USA

 Record created 2017-06-28, last modified 2018-09-13

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