Reliability Analysis of MTJ-based Functional Module for Neuromorphic Computing

摘要

The power and reliability issues of today's memories limit the improvements attained by their implementation in scaled technology nodes. Several emergent memory technologies attempt to address the technical constraints of today's memories, amongst which, one of the most promising solutions is the Spin-Transfer-Torque Magnetic Random Access Memories (STT-MRAMs). One of the great advantages of the emerging memories is that they favor increasing system complexity and performance. New applications and computation paradigms, such as neuromorphic computing, unfeasible a few years back due to technological limitations, can take profit from this technology. Intensive research has been conducted recently related to magnetic device physics and its implementation as dedicated hardware for neuromorphic computing, however, little work has been conducted to evaluate the reliability of such circuits. In this paper we investigate the effect of meaningful MTJ reliability issues on the behavior of an MTJ-based Spiking Neural Network.