Abstract Scope |
The exponentially improving performance of conventional digital computers has slowed in recent years due to the speed and power consumption issues that are largely attributable to the von Neumann bottleneck (i.e., the need to transfer data between spatially separate processor and memory blocks). In contrast, neuromorphic (i.e., brain-like) computing aims to circumvent the limitations of von Neumann architectures by spatially co-locating processor and memory blocks or even combining logic and data storage functions within the same device. In addition to reducing power consumption in conventional computing, neuromorphic devices also provide efficient architectures for emerging applications such as image recognition, machine learning, and artificial intelligence. This talk will explore how the reduced dielectric screening in low-dimensional nanoelectronic materials enables opportunities for novel gate-tunable neuromorphic devices. Overall, this work introduces new foundational circuit elements for neuromorphic computing in addition to providing alternative pathways for utilizing the unique quantum characteristics of low-dimensional nanoelectronic materials. |