Event-Driven Inference with Spiking Neural Networks Enhanced by Non-Volatile Analog Spin-Orbit Torque Devices

Edge computing is becoming increasingly important, with the major challenge being the development of edge devices capable of performing tasks, such as classification and prediction, with high energy efficiency overcoming the limitations of the traditional von Neumann architecture. We propose an inference system based on a liquid state machine that combines spiking neural networks with non-volatile analog memory devices, inspired by information processing in the biological brain. Multiply accumulate operations were successfully demonstrated using three-terminal analog spintronics devices with a spin-orbit torque-induced magnetization switching as analog synaptic weights. This system operates in an event-driven manner based on spike events. Because the memory is non-volatile, the static power consumption is reduced to almost zero. This characteristic is particularly effective for edge devices that process low-frequency sensor signals, such as those used for keyword spotting or environmental monitoring.