Phase change memory as synapse for ultra-dense neuromorphic systems: Application to complex visual pattern extraction

TLDR

The study demonstrates an energy‑efficient approach using Phase Change Memory as synapses in ultra‑dense neuromorphic systems, proposes a 2‑PCM synapse architecture with tailored read/write/reset schemes, and introduces a versatile behavioral model for large‑scale neural simulations. PCM devices with various chalcogenide materials were characterized for synaptic behavior, and multi‑physical simulations were employed to interpret the results, supporting the proposed architecture and model. The authors achieve the first demonstration of complex visual pattern extraction from real‑world data using PCM synapses in a two‑layer spiking neural network, and provide a system power analysis across different scaled PCM technologies.

Abstract

We demonstrate a unique energy efficient methodology to use Phase Change Memory (PCM) as synapse in ultra-dense large scale neuromorphic systems. PCM devices with different chalcogenide materials were characterized to demonstrate synaptic behavior. Multi-physical simulations were used to interpret the results. We propose special circuit architecture ("the 2-PCM synapse"), read, write, and reset programming schemes suitable for the use of PCM in neural networks. A versatile behavioral model of PCM which can be used for simulating large scale neural systems is introduced. First demonstration of complex visual pattern extraction from real world data using PCM synapses in a 2-layer spiking neural network (SNN) is shown. System power analysis for different scaled PCM technologies is also provided.