Magnetic Tunnel Junction Enabled All-Spin Stochastic Spiking Neural Network

Gopalakrishnan Srinivasana, Abhronil Senguptab and Kaushik Royc
School of Electrical and Computer Engineering, Purdue University.


Biologically-inspired spiking neural networks (SNNs) have attracted significant research interest due to their inherent computational efficiency in performing classification and recognition tasks. The conventional CMOSbased implementations of large-scale SNNs are power intensive. This is a consequence of the fundamental mismatch between the technology used to realize the neurons and synapses, and the neuroscience mechanisms governing their operation, leading to area-expensive circuit designs. In this work, we present a threeterminal spintronic device, namely, the magnetic tunnel junction (MTJ)-heavy metal (HM) heterostructure that is inherently capable of emulating the neuronal and synaptic dynamics. We exploit the stochastic switching behavior of the MTJ in the presence of thermal noise to mimic the probabilistic spiking of cortical neurons, and the conditional change in the state of a binary synapse based on the pre- and post-synaptic spiking activity required for plasticity. We demonstrate the efficacy of a crossbar organization of our MTJ-HM based stochastic SNN in digit recognition using a comprehensive device-circuit-system simulation framework. The energy efficiency of the proposed system stems from the ultra-low switching energy of the MTJ-HM device, and the in-memory computation rendered possible by the localized arrangement of the computational units (neurons) and non-volatile synaptic memory in such crossbar architectures.

Keywords: Spiking neural networks, Probabilistic spiking neuron, Stochastic STDP, Magnetic tunnel junction.

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