Brain-like associative learning using a nanoscale non-volatile phase change synaptic device array

TL;DR

This paper demonstrates a brain-like associative learning system using nanoscale phase change memory devices in an array, showing robustness to device variations and energy efficiency tradeoffs.

Contribution

It provides the first experimental demonstration of array-level associative learning with phase change memory devices, bridging the gap between device-level work and network-level applications.

Findings

Array-level associative learning was successfully implemented experimentally.

The system is robust to device variations and can adapt with more training epochs.

Energy consumption decreases with lower variation tolerance.

Abstract

Recent advances in neuroscience together with nanoscale electronic device technology have resulted in huge interests in realizing brain-like computing hardwares using emerging nanoscale memory devices as synaptic elements. Although there has been experimental work that demonstrated the operation of nanoscale synaptic element at the single device level, network level studies have been limited to simulations. In this work, we demonstrate, using experiments, array level associative learning using phase change synaptic devices connected in a grid like configuration similar to the organization of the biological brain. Implementing Hebbian learning with phase change memory cells, the synaptic grid was able to store presented patterns and recall missing patterns in an associative brain-like fashion. We found that the system is robust to device variations, and large variations in cell resistance states can be accommodated by increasing the number of training epochs. We illustrated the tradeoff between variation tolerance of the network and the overall energy consumption, and found that energy consumption is decreased significantly for lower variation tolerance.