A Novel Compound Synapse Using Probabilistic Spin–Orbit-Torque Switching for MTJ-Based Deep Neural Networks

Abstract

Analog electronic nonvolatile memories mimicking synaptic operations are being explored for the implementation of neuromorphic computing systems. Compound synapses consisting of ensembles of stochastic binary elements are alternatives to analog memory synapses to achieve multilevel memory operation. Among the existing binary memory technologies, magnetic tunneling junction (MTJ)-based magnetic random access memory (MRAM) technology has matured to the point of commercialization. More importantly for this work, stochasticity is natural to the MTJ switching physics, e.g., devices referred to as p-bits that mimic binary stochastic neurons. In this article, we experimentally demonstrate a novel compound synapse that uses stochastic spin-orbit-torque (SOT) switching of an ensemble of nanomagnets that are located on one shared spin Hall effect (SHE) material channel, i.e., tantalum. By using a properly chosen pulse scheme, we are able to demonstrate linear potentiation and depression in the synapse, as required for many neuromorphic architectures. In addition to this experimental effort, we also performed circuit simulations on an SOT-MRAM-based 784 × 200 × 10 deep belief network (DBN) consisting of p-bit-based neurons and compound synapses. MNIST pattern recognition was used to evaluate the system performance, and our findings indicate that a significant reduction in recognition error rates can be achieved by improving the linearity of the potentiation and depression curves using an incremental pulse scheme.

Keywords

• Magnetic random access memory (MRAM)
• probabilistic computing
• spintronics
• spin–orbit torque (SOT)
• synapse