부산대학교 도서관

With the rapidly evolving internet of things (IoT) era, the ever-rising demand for data transfer and storage has put a knotty problem on conventional computers, known as the von Neumann bottleneck and memory wall problem. Slow scaling of CMOS transistors due to physical and economical limitations further exacerbates the situation. It is only logical to mimic what has been known so far as the most energy-efficient system, the human brain. The brain-inspired neuromorphic computing systems compute and store the data locally, which dramatically reduces area and energy consumption. In this work, we demonstrate thermal-induced multi-state memristors for neuromorphic engineering applications. We show that in a neural network that uses a memristor-spintronic nano oscillator connection to implement the synapse-neuron pair, with increased temperature, the total power consumption could be reduced by more than 50 % without degrading the output power of a spintronic-based neuron.