Integrated neuromorphic computing networks by artificial spin synapses and spin neurons

Abstract

One long-standing goal in the emerging neuromorphic field is to create a reliable neural network hardware implementation that has low energy consumption, while providing massively parallel computation. Although diverse oxide-based devices have made significant progress as artificial synaptic and neuronal components, these devices still need further optimization regarding linearity, symmetry, and stability. Here, we present a proof-of-concept experiment for integrated neuromorphic computing networks by utilizing spintronics-based synapse (spin-S) and neuron (spin-N) devices, along with linear and symmetric weight responses for spin-S using a stripe domain and activation functions for spin-N. An integrated neural network of electrically connected spin-S and spin-N successfully proves the integration function for a simple pattern classification task. We simulate a spin-N network using the extracted device characteristics and demonstrate a high classification accuracy (over 93%) for the spin-S and spin-N optimization without the assistance of additional software or circuits required in previous reports. These experimental studies provide a new path toward establishing more compact and efficient neural network systems with optimized multifunctional spintronic devices. Neuromorphic computing: A new spin on synapses and neuronsAn array of low-power devices that mimic the behavior of the brain has been constructed by researchers in South Korea. The brain works by passing chemical and electrical signals across a network of cells called neurons, which are connected via synapses. Scientists want to create an artificial neural network to take advantage of the parallel way in which the brain processes information. YeonJoo Jeong from the Korea Institute of Science and Technology, Jin Pyo Hong from Hanyang University, both in Seoul, and their colleagues have demonstrated a proof-of-principle neuromorphic computing network using so-called spintronic devices. Spintronics are low-power devices that use the property of an electron called spin rather than its charge as in conventional electronics. The team's neural network of electrically connected spin-synapses and spin-neurons successfully performed a simple pattern classification task. We introduced spintronics-based synapses (spin-S) by utilizing a stripe domain ensuring its highly linear and symmetric weight responses, together with domain-wall motion-based neurons (spin-N) for activation functions. In addition, a crossbar array architecture for the spin-S/N has been proposed and experimentally demonstrated. A simple pattern-classification task was tested using an integrated network of electrically connected spin-S and spin-N to mimic a human brain. Our experimental findings provide a new avenue toward establishing more efficient neural network systems with spintronic devices.