Opportunities and challenges on nanoscale 3D neuromorphic computing system

Abstract

Scaling down the transistor to gain more computation power will eventually reach the unsurmountable physical limitation. Neuromorphic Computing is a novel and promising computing scheme emulating the nervous structure and data processing methodology of a human brain. This paper presents the comparison between the conventional Von Neumann architecture and the neuromorphic computing architecture. We propose a novel 3D neuromorphic computing architecture combining the nanoscale device “memristor” and monolithic 3D integration technology, which could reduce system power consumption, provide high-connectivity, resolve routing congestion issues, and offer massively parallel data processing capability. We present the electrical modeling and simulation of our proposed nanoscale 3D neuromorphic computing structures. We also discuss the challenges in the field of neuromorphic computing design. In order to design a cognitive computing system by mimicking human brain functionalities, we present three novel architectures, which are notated as distributed neuromorphic computing architecture, cluster neuromorphic computing architecture, and associative neuromorphic computing architecture.