The study discusses the challenges and potential applications of edge AI, which processes real-time data at the edge, rather than on the cloud.

It highlights the need for efficient and cost-effective technology for effective edge computing, such as self-driving cars and machine anomaly prediction.

The study presents a novel memrisor device that adjusts its response timescale through voltage variation, enhancing its learning capabilities, making it a promising option for edge computing.