Tunable Neuromorphic Computing for Dynamic Multi-Timescale Sensing in Motion Recognition.

Abstract

Motion recognition, especially the distinction between high-speed and low-speed movements, is a challenging computational task that typically requires substantial resources. The extensive response range required to detect such variations in speed often exceeds the capabilities of traditional CMOS technology. This report introduces a SnS₂-based in-sensor reservoir that offers an effective solution for detecting a variety of motion types at sensory terminals. By leveraging in-sensor reservoir computing, the device excels at classifying different motions across a wide velocity spectrum, providing a novel and promising method for motion recognition. The conductance of SnS₂ channel under light stimulation is governed by the trapping and recombination of photogenerated carriers at the inherent defect states, which contributes to the flexible optically dynamical sensing function of the device to varying illumination times. These attributes make the device versatile for both optical sensing and synaptic emulation. The findings suggest that such a SnS₂-based device could be instrumental in advancing motion recognition capabilities for developing next-generation artificial intelligence systems.