Summary
Generative AI, such as ChatGPT, along with AI image and video generation platforms, have recently taken the world by storm. However, recent studies have revealed that running AI engines consumes a staggering amount of energy. Neuromorphic systems, which utilize memristors and crossbar arrays, leverage Computing-in-Memory (CIM) technology for AI computation, demonstrating significant improvements in energy efficiency. The goal of this project is to investigate memristor-based CIM for neuromorphic systems from the perspectives of design, optimization, and fabrication. This project offers a uniqu
